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Device and method for decreasing web breaks in a printing system

1. 250 571 5 414 798 5 1995 Nigawara et 395 75 5 457 515 10 1995 Quadracci et al 355 32 5 521 844 5 1996 Karis 364 551 01 5 534 281 7 1996 Pappas et al 426 383 5 652 388 7 1997 Callan et al 73 628 5 604 524 12 1997 Evans 706 2 5 781 195 7 1998 Marvin 345 428 5 791 541 8 1998 Jituishi et et 226 40 OTHER PUBLICATIONS Buntine Operations on Counting Tables Turning Institute 1 4 Oct 1989 Buntine Review and Questions on Learning Decision Trees Turning Institute pp 1 6 Oct 1989 Chou Optimal Partitioning for Classification and Regres sion Trees 13 IEEE Transactions on Pattern Analysis and Machine Intelligence pp 340 354 1991 Buntine Learning Classification Trees RIACS amp NASA Ames Research Center pp 1 25 Nov 1991 Oliver Decision Graphs An Extension of Decision Trees Draft Monash University Australia pp 1 1 Dec 15 1992 Buntine et al Introduction to IND Version 2 1 and Recur sive Partitioning IND User Manual NASA Ames Research Center Dec 31 1992 including a one page summary of IND Version 2 0 and a two page summary of modifications to IND Versions 0 1 to 2 1 Buntine Tree Classification Software presented at The Third National Technology Transfer Conference and sition Baltimore pp 1
2. 250 559 4 4 359 178 11 1982 Hayashi et al 226 25 4 532 500 7 1985 Henk 340 675 4 655 093 4 1987 Strom et al 73 862 41 4 716 978 1 1988 Wales et al s 177 25 4 752 897 6 1988 Zoeller et al 702 40 4 846 060 7 1989 Proctor 101 228 4 892 426 1 1990 Steele 400 708 SPECIFY A PARTICULAR RESULT OR PROBLEM 2 ASSOCIATED WITH PROCESS DEFINE CLASSES BASED ON THE OCCURRENCE OF THE 34 PARTICULAR RESULT IDENTIFY PROCESS ATTRIBUTES WHICH 36 POTENTIALLY AFFECT THE PARTICULAR RESULT 8 DEFINE CLASS IDENTIFY NEW CONTEXT HEURISTICS ATTRIBUTES ADD CHANGE DELETE HEURISTICS COLLECT DATA ASSOCIATED WITH EACH 40 ATTRIBUTE FOR A NUMBER OF RUNS OF THE PROCESS AND INDICATE THE CLASS RUN COLLECT DATA FOR 60 ADDITIONAL RUNS OF THE PROCESS BUILD AN INDUCTION TREE List continued on next page OTHER PUBLICATIONS Bareiss et al Supporting Start to Finish Development of Knowledge Bases 4 Machine Learning 259 283 1989 Clark et al The CN2 Induction Algorithm 3 Machine Learning pp 261 283 1989 Kasunich et al Gravure Process and Technology Chs 4 6 9 11 13 15 pp 37 64 99 241 259 362 379 398 Brett Rutherfored ed 1991 Draper Assessment and Propagation of Model Uncertainty dr
3. MONITOR PROCESS IS OUTCOME OF PROCESS DESIRABLE FIG 3B 0 5 Patent Dec 28 1999 Sheet 5 of 8 6 009 421 TERMINATION CONDITION OR USER TERMINATES PARITION ATTRIBUTE VALUES INTO VALUE GROUPS COMPARE ATTRIBUTES TO CLASS HEURISTICS CALCULATE AND REPORT INFO GAIN FOR ALL ATTRIBUTES ACTIVE WITHIN CONTEXT HEURISTICS ARE THERE ATTRIBUTES WITH POSITIVE INFO GAIN USER SELECTS ATTRIBUTE WITH POSITIVE INFO GAIN DEFINE NEW NODES FROM ATTRIBUTE VALUE GROUPS SELECT A NEW NODE DETERMINE DATABASE FOR THE NEW NODES FIG 4 0 5 Patent Dec 28 1999 Sheet 6 of 8 6 009 421 SELECT ATTRIBUTE 122 NO WITHIN CONTEXT HEURISTICS IS SELECTED ATTRIBUTE REAL VALUED 156 YES HAVE ALL ATTRIBUTES BEEN SELECTED NO FORM DATA SET 1 ALL ATTRIBUTE VALUES OF C1 2 ALL ATTRIBUTE VALUES OF C2 SORT 1 AND S2 IN ASCENDING ORDER DETERMINE MEDIANS M1 OF 1 M2 OF 2 NO 140 SWITCH TES LABELS 142 1 lt gt S2 NO 1 gt M2 1 1 143 2 M2 U S Patent PARTITION ATTRIBUTES INTO VALUE GROUPS USING NATURAL CATEGORIES FIG Dec 28 1999 Sheet 7 of 8 6 009 421 REDEFINE DATA SETS 1 ALL 1 VALUES gt 1 S2 ALL 2 VALUES lt 2 1 MS1 M2 MS2 144 DETERMINE MEDIANS MS1 FOR 1 126 2 2 YES PARTITION ATTRIBUTE VALUES INTO VALUE 150 GROUPS USING M1 AND M2 A
4. 140 tests to determine if the median 1 is greater than the median M2 If so a block 142 relabels the data set S1 as data set S2 and the median M1 as median M2 and simultaneously relabels the data set S2 as data set S1 and the median M2 as median M1 Furthermore the block 142 stores a class flag that indicates that the data sets S1 and S2 have been relabeled Next a block 143 sets median values MS1 and MS2 equal to medians M1 and 2 respectively Ablock 144 of FIG 5B redefines the data set S1 to include only the values within the data set S1 that are greater than or equal to the median 1 The block 144 also redefines the data set S2 to include only the values within the data set S2 which are less than or equal to the median MS2 Furthermore the block 144 sets the medians M1 and M2 equal to the medians MS1 and MS2 respectively A block 146 then determines the medians MS1 and MS2 of the new data sets S1 and S2 respectively Next block 148 determines whether the median MSI is greater than or equal to the median 2 and if not control returns to the block 144 which redefines the data sets S1 and S2 The blocks 144 146 and 148 are re executed until the block 148 determines that the median 1 is greater than or equal to the median MS2 When this condition occurs a block 150 partitions the selected real valued attribute into three value groups The first value group includes all of those attribute values associated with records
5. Of course when a particular correlation between one or a combination of two or more attributes which may be both printing and web manufacturing attributes is identified this correlation may be displayed via a printer a monitor or other display device and may be used to control the printing system to avoid occurrence of web breaks Furthermore when a correlation between one or more web manufacturing attributes and web breaks or other problems is identified a web manufacturer may be notified that there may be a problem in for example a particular web manufacturing machine or auxiliary machine because there is an increased occurrence of web breaks in the webs manufactured or processed by that machine In such a case the web manufacturer may perform a detailed analysis of the suspect machine or process to determine if a heretofore undetected problem exists and then fix the machine or change the process when a problem is detected A preferred method and device for analyzing collected data pertaining to either or both of web manufacturing attributes and printing attributes particularly web tensions to identify correlations between one or a combination of two or more of these attributes and web breaks or other problems is discussed hereinafter Generally a preferred correlation analysis uses an expert system and particularly a decision tree induction analysis to determine the correla tion between any one or more web manufacturing an
6. a multiplicity of reels of material e g four on any particular date Each reel is logically divided into a number of reel sets e g three generally associated with different continuous portions of the material on the reel For example the first one third of the material when unrolled from the reel may be the first reel set the second one third may be the second reel set and the last one third may be the third reel set To produce separate webs from the material on the reel each reel set is unrolled from the reel is cut along the length thereof to divide the width of the material into a number of log positions and the material at each log position of each reel set is rolled onto a smaller reel to produce the web reels that are delivered to customers such as printers Typically the first log position of each reel set is designated as the log position the second log position is designated as the B log position and so on However generally the last log position is designated as the 7 log position no matter how many log positions there actually are in the reel set Of course each reel set may be cut into any desired number of log positions to produce webs of any desired width Furthermore while being unrolled the material at each of the log positions of each reel set may be processed by other particularly iden tified auxiliary machines such as a coater a cutter a winder 6 009 421 9 supercolander etc
7. are active within the context heuristics have been selected If so the method proceeds to the block 108 of FIG 4 Otherwise the block 122 selects the next attribute for partitioning 10 15 25 30 35 40 45 50 55 60 65 20 Referring now to FIG 6 an example induction tree 200 illustrating the operation of the above described decision tree induction method for the case in which data has been collected and stored for printing attributes and web manu facturing attributes is shown In particular the induction tree 200 was created using among others web tension attribute data infeed tension and drag tension and web manufactur ing attribute data log position The induction tree 200 includes a root 201 and a summary box 202 that indicates that of the 113 records within the current database at the root 201 99 of the records are associated with the no web break NO WB class while 14 of the records are associated with the web break WB class At the time that the induction tree 200 was constructed the normalized information gain val ues were computed for each attribute active within the context heuristics and the user was presented with a list of the attributes active within the context heuristics and the normalized information gain values associated therewith In the case shown in FIG 6 the user chose the attribute Infeed Tension as a first branch 203 of the induction tree 200 The attribute Infeed Tension
8. blocks 105 and 106 which select the next node to be examined If however one or more of the attributes have a positive normalized information gain value a block 112 presents each of the attributes active within the context heuristics and the normalized information gain value asso ciated therewith to the expert via the display 23 of FIG 2 6 009 421 17 Preferably the attributes are ranked according to the normalized information gain values associated therewith Such ranking may include the categories of BEST for the attribute having the highest normalized information gain value HIGHLY USEFUL for attributes having a normal ized information gain value at least 95 percent of the highest normalized information gain value USEFUL for attributes having a normalized information gain value between 90 and 95 percent of the highest normalized information gain value MARGINAL for attributes having a normalized informa tion gain value between 75 and 90 percent of the highest normalized information gain value QUESTIONABLE for attributes having a normalized information gain value between 50 and 75 percent of the highest normalized infor mation gain value LAST RESORT for attributes having a normalized information gain value above zero but below 50 percent of the highest normalized information gain value and USELESS for attributes having a normalized informa tion gain value of substantially zero Any other desired categories can be alter
9. contact the web manufacturer to inform the web manufacturer that there may be a problem with the webs made from the A log position of a particular machine at a particular mill site and or may refuse to purchase such webs to reduce the occurrence of web breaks Of course other induction trees can be produced to determine other correlations between one or more printing attributes such as web tensions and web manufacturing attributes such as mill site log position web tensile strength web moisture content coefficient of friction etc as well as to determine other web tension ratio values or ranges In some cases different values or ranges for the same web tension ratio may be determined In these cases the different values or ranges may be combined into a single range or alternatively a single best value or range may be determined from the different values or ranges in any desired manner e g averaging Furthermore if desired other types of analyses could be performed to determine correla tions between one or more printing and or web manufactur ing attributes and the occurrence of web breaks or other problems in a printing system and to determine appropriate web tension ratios for decreasing the occurrence of web breaks in a printing system Other such systems include but are not limited to standard correlation analyses neural networks fuzzy logic systems or any expert system that stores and uses data pertaining to one or
10. for the plurality of printing runs and wherein the implementing means includes means for determining from the stored data and the further stored data a second tension value for the second location at which a web break in the printing system is less likely to occur when the tension of the web at the first location is at the determined first tension value 20 The device of claim 19 further including an output device that produces an indication of the determined first tension value and the determined second tension value 21 Amethod for use in reducing the occurrence of breaks in a web of a printing system comprising the steps of collecting data pertaining to values of web tensions at first and second locations within the printing system during a plurality of runs of the printing system and determining from the collected data a value of a web tension ratio for which occurrence of breaks in the web is reduced during the plurality of runs of the printing system wherein the web tension ratio comprises a ratio of the web tension at the first location to the web tension at the second location 22 The method of claim 21 further including the step of controlling the printing system to keep the web tension ratio at the determined web tension ratio value 23 The method of claim 22 wherein the determined web tension ratio value comprises a web tension ratio range 24 The method of claim 21 wherein the step of deter mining includes the step
11. is a reel device and the determining means determines the infeed to reel tension ratio 35 The device of claim 29 wherein the first location is a ribbon device and the second location is a drag device and the determining means determines the ribbon to drag tension ratio 36 The device of claim 29 wherein the indicating means includes a display that displays a message to a user to indicate when the determined tension ratio is outside of the predetermined tension ratio range
12. is real valued and was separated into three ranges identified as nodes 204 206 and 208 using the real valued discretizing routine described above with respect to FIGS 5A and 5B As illustrated in FIG 6 the node 204 is associated with the infeed tension range of less than 1 84 PLI the node 206 is associated with the infeed tension range of less than or equal to 1 88 PLI and greater than or equal to 1 84 PLI and the node 208 is associated with the infeed tension range of greater than 1 88 PLI At the node 204 the current database included 59 records comprising the records within the database at the root 201 having an Infeed Tension value less than 1 84 PLI A summary block 210 indicates that 55 of these records were associated with the NO class and that 4 of these records were associated with the WB class Following the node 204 the attribute Log Position was selected as a branch 212 The attribute Log Position was divided into two discrete value groups which are identified as nodes 214 and 216 The node 214 is associated with the and log positions of web material as manufactured on a particular printing machine at a particular mill site while the node 216 is associated with the Z or last log position of the web material associated with that same printing machine As illustrated by the summary block 218 the A and log positions had 14 records associated with the NO WB class 2 records associated wi
13. more such attributes for printing runs in which the problem occurred and for printing runs in which the problem did not occur The commercial software product known as Knowledg eSEEKER manufactured by Angoss Software International Limited is one such expert system Of course when determining an appropriate web tension ratio range the system preforming the correlation analysis may store data indicating the web tensions at particular web locations and use this data to determine an appropriate web 10 15 20 25 30 35 40 45 50 55 60 65 22 tension ratio range as for example described above and illustrated in FIG 6 with respect to the drag tension to infeed tension ratio In some cases the correlation analysis may also use for example appropriate software to calculate web tension ratios from the stored web tension data and to determine correlations between these calculated web tension ratios and web breaks Alternatively the database may store web tension ratios directly and these web tension ratios may be used to determine one or more appropriate web tension ratio values and or ranges While the present invention is specifically described herein for the purpose of identifying the causes of and reducing the occurrence of web breaks in a printing system that prints on a paper web the attributes and methods described herein may be equally used to identify the causes of and to decrease the occurrence of w
14. of Low Voltage Power and Control Circuits IEEE Transactions on industry applica tions vol 27 No 5 Sep 1 1991 0 5 Patent Dec 28 1999 Sheet 1 of 8 6 009 421 N c B i 17 CONTROLLER 8 FIG 1 FREI S he ay HW E g H g H KIAT 2 Lu r 2 Hz y OX 0 5 Patent Dec 28 1999 Sheet 2 of 8 6 009 421 20 KEYBOARD 21 22 23 0 5 Patent Dec 28 1999 Sheet 3 of 8 6 009 421 SPECIFY A PARTICULAR RESULT OR PROBLEM ASSOCIATED WITH A PROCESS DEFINE CLASSES BASED ON THE OCCURRENCE OF THE PARTICULAR RESULT IDENTIFY PROCESS ATTRIBUTES WHICH POTENTIALLY AFFECT THE PARTICULAR RESULT 64 38 DEFINE CLASS AND IDENTIFY NEW CONTEXT HEURISTICS ATTRIBUTES COLLECT DATA ADD CHANGE DELETE ASSOCIATED WITH EACH HEURISTICS ATTRIBUTE FOR A NUMBER OF RUNS OF THE PROCESS AND INDICATE THE CLASS COLLECT DATA FOR OF EACH RUN ADDITIONAL RUNS OF THE PROCESS BUILD AN INDUCTION TREE N DECISION YES e FIG 3A 0 5 Patent Dec 28 1999 Sheet 4 of 8 6 009 421 LOCATE PATH WITHIN INDUCTION TREE INDICATING THAT THE 48 PARTICULAR RESULT IS MORE LIKELY TO OCCUR THAN NOT ADD ELEMENTS OF THE 50 PATH TO SOLUTION SATISFIED WITH SOLUTION INCORPORATE SOLUTION INTO PROCESS CONTROL
15. reel 11 and then through the printing stations 8 which print an image on the other side of the web 12 Preferably there are five printing stations 7 and 8 for each side of the web 12 Each of the printing stations 7 and 8 includes printing cylinders that print one of cyan magenta yellow or key black ink onto the web 12 or that print type onto the web 12 However any other number of printing stations 7 and or 8 may be used for printing any other colors or image types onto the web 12 After being delivered through the printing stations 7 and 8 the web 12 is delivered over a drag roller 14 and is then cut along the length thereof by a the ribbon cutter 9 into for example four ribbons of equal width Each of the ribbons is fed over or around a ribbon roller 15 and is then compiled or stacked with the other ribbons in the folder cutter 10 Thereafter the stacked ribbons are cut along the width thereof to form a set of pages or signatures that are folded into a book which in turn is delivered to a mail table 16 for delivery to a customer all as generally known in the art During operation of the printing system 5 power is applied to the reel 11 to the infeed roller 13 to the drag roller 14 and to the ribbon rollers 15 to control movement of the web 12 through the printing system 5 Of course to assure that power is transferred to the web 12 at these locations the web 10 is held taught against these rollers which in turn creates a t
16. result it is advisable to identify the particular predetermined tension ratio value or range that is appropriate for reducing web breaks in each different printing system for each tension ratio upon which tension control is being based While such tension ratio values or ranges may be determined by for example trial and error methods or any other desired 10 15 20 30 35 40 45 50 55 60 65 8 method an appropriate tension ratio value and or range for any particular tension ratio is preferably determined from data indicating relevant tensions or tension ratios of the web 12 for prior runs of the printing system 5 in which both web breaks occurred and web breaks did not occur Thus according to a preferred embodiment a database which may be located in the controller 17 or elsewhere stores data indicating two or more web tensions or one or more web tension ratios for a plurality of printing runs along with an indication of whether a web break occurred or did not occur at those tensions or tension ratios within each of the plurality of printing runs Typically a printing run in this context is defined by printing associated with one entire reel 11 i e printing associated with each reel 11 loaded onto the reel stand 6 of the printing system 5 The collected and stored data may indicate for example two or more of the reel tension the infeed tension the drag tension and the ribbon tensions or one or mo
17. within the current database that are less than or equal to M1 The second value group includes all of those attribute values associated with records within the current database that are greater than M1 and less than M2 The third value group includes all of those attribute values associated with records within the current database that are greater than or equal to M2 If desired additional value groups can be defined by ranges at the upper and or lower ends of the attribute value continuum that are associated exclusively with one class Although the blocks 134 and 146 are described herein as determining the medians of the sets S1 and S2 any other desired statistical properties of the sets S1 and S2 including the means thereof could instead be determined and used in 6 009 421 19 the method illustrated in the flowchart of FIGS 5A and 5B It should be noted that the above described method of partitioning real valued attributes is computationally simple and inexpensive and therefore can be applied at every node of the induction tree that is labeled as a branching point block 152 determines whether the distribution of the classes among the value groups developed by the blocks 126 and 150 is consistent with any class heuristics previously identified at the steps 38 or 62 of FIG 3A For real valued attributes it is assumed that the first class is associated with the data set S1 meaning that proportionately more of the values within th
18. 10 Dec 1992 XpertRule amp Analyser M Knowledge from Data Users Guide Release 2 published by Attar Software 1994 ANGOSS KnowledgeSeeker Version 3 0 User s Guide published by ANGOSS Software Intl Ltd 1984 1994 Buntine Recursive Partitioning Skeleton Code pp 1 2 1990 Recursive Partitioning Skeleton Code pp 1 2 1990 Michalski et al Learning by Being Told and Learning From Examples An Experimental Comparison of the Two Meth ods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis 4 Inter national Journal of Policy Analysis and Information Systems 125 161 1980 Musen Automated Support for Building and Extending Expert Models 4 Machine Learning 347 375 1989 Shannon et al The Mathematical Theory of Communica tion pp 1 5 30 64 1963 Tan et aL Two Case Studies in Cost Sensitive Concept Acquisition in Proceedings of the Eighth National Confer ence on Artificial Intelligence 854 860 1990 Walsh Handbook of Nonparametric Statistics pp 10 50 194 195 290 300 1962 Weiss et al Computer Systems that Learn Chs 5 7 pp 113 203 1991 Anderson The Adaptive Character of Thought pp 1 5 40 1990 Fayyad et aL On the Handling of Continuous Valued Attributes in Decision Tree Generation 8 Machine Learn ing pp 87 102 1992 Mantaras A Distance Based Attribute Selection Measure for Decision Tree Induction 6 Mac
19. S BREAK POINTS ARE CLASSES ASSOCIATED WITH THE VALUE GROUPS CONSISTENT WITH THE CLASS HEURISTIC FOR THIS ATTRIBUTE YES MARK 5B 0 5 Patent Dec 28 1999 Sheet 8 of 8 6 009 421 NO WB 99 202 WB 14 Y Nx lt 184 1 84 lt X lt 1 88 X gt 1 88 4 208 206 NO WB 55 NO WB 30 NO WB 14 wB4 210 wsB2 WB 8 LOG LOG POSITION 212 POSITION 232 NO WB 14 NO WB 41 NO WBO NO WB 14 wB2 218 wB2 220 WB 5 WB 3 DRAG TENSION 2555 NO WB38 NO WB3 FIG 6 6 009 421 1 DEVICE AND METHOD FOR DECREASING WEB BREAKS IN A PRINTING SYSTEM BASED ON WEB TENSION RATIOS RELATED APPLICATION This is a continuation in part of U S patent application Ser No 08 196 707 filed Feb 15 1994 now U S Pat No 5 694 524 TECHNICAL FIELD The present invention relates generally to printing sys tems and more particularly to a method and device that identifies conditions leading to and that decreases the occur rence of web breaks within a printing system BACKGROUND ART Large scale printing systems such as rotogravure printing presses feed a continuous web of material typically paper through printing machinery that forces the web into contact with one or more rotogravure printing cylinders which in turn print images onto the web in a standard manner Thereafter the web is cut into individual pages or signatures which are collated to produce for example newspapers books magazines etc A
20. S OF THE ps0 PATH TO SOLUTION 52 SATISFIED WITH SOLUTION YES INCORPORATE SOLUTION 70 PROCESS CONTROL MONITOR PROCESS 2 IS OUTCOME OF No PROCESS DESIRABLE 6 009 421 Page 2 U S PATENT DOCUMENTS 4 961 089 10 1990 Jamzadeh 399 165 5 036 706 8 1991 Gnuechtel et al 73 597 5 130 557 7 1992 250 559 12 5 136 686 8 1992 395 13 5 162 119 11 1992 Pappas et al 425 92 5 163 371 11 1992 Kotterer et al 101 484 5 188 028 2 1993 Reichel 101 228 5 243 408 9 1993 Whitman III 356 430 5 266 995 11 1993 Quadracci et 355 77 5 269 222 12 1993 Johnson et al 101 228 5 280 720 1 1994 Heidt sse 73 59 5 282 261 1 1994 Skeirik 706 23 5 285 238 2 1994 Quadracci et al 355 77 5 301 866 4 1994 Veh et al wees 226 11 5 325 445 6 1994 Herbert 382 225 5 325 774 7 1994 Breton et al 101 228 5 357 812 10 1994 Kiibert et al 73 862 5 365 796 11 1994 Lin etal 73 862 5 365 844 11 1994 Miyashige 101 228 5 378 948 1 1995 Ottl
21. US006009421A United States Patent 1 Patent Number 6 009 421 Xie et al 4 Date of Patent Dec 28 1999 54 DEVICE AND METHOD FOR DECREASING 4 959 040 9 1990 Gardner et al 474 103 WEB BREAKS IN A PRINTING SYSTEM BASED ON WEB TENSION RATIOS 75 Inventors Zhenhua Xie Naperville Did Bun Wong Glen Ellyn both of Ill Robert Evans Gallatin Tenn 73 Assignee R R Donnelley amp Sons Company Lisle Ill 21 Appl No 08 847 114 22 Filed May 1 1997 Related U S Application Data 63 Continuation in part of application No 08 196 707 Feb 15 1994 Pat No 5 604 524 51 Int CLS arte acceptos G06F 17 00 52 10 8 Cl 706 61 706 904 706 903 706 906 58 Field of Search 706 61 12 23 706 903 904 906 56 References Cited U S PATENT DOCUMENTS Re 31 312 7 1983 Eddens 73 862 48 3 763 373 10 1973 Baber 250 559 45 242 75 44 3 822 838 7 1974 Butler Jr et al 3 823 282 7 1974 Bregar see 200 61 18 3 823 283 7 1974 Larson 200 61 18 3 862 724 1 1975 Johnson et al we 242 442 1 3 906 232 9 1975 Meihofer 250 341 8 3 928 844 12 1975 Meihofer 226 11 3 950 988 4 1976 Nowisch et al 73 144 4 130 014 12 1978 Eddens 73 144 4 186 309 1 1980 Gnuechtel 250 561 4 335 316 6 1982 Glanz et al
22. aft version in Proceedings of the Fourth International Workshop on Artificial Intelligence and Statistics pp 497 509 1993 Edelstein Mining for Gold 6 pgs Apr 21 1997 Simoudis Reality Check for Data Mining IEEE Expert pp 25 33 Oct 1996 List continued on next page Primary Examiner Tariq R Hatiz Assistant Examiner Wilbert L Starks Jr Attorney Agent or Firm Marshall O Toole Gerstein Murray amp Borun 57 ABSTRACT A device and a method use a decision tree induction analysis to determine a web tension ratio range for which web breaks are less likely to occur within a printing system based on web tension data collected from a plurality of previous runs of the printing system The device and method also measure two are more web tensions during operation of the printing system calculate a web tension ratio and compare that calculated web tension ratio with the determined web ten sion ratio range When the calculated web tension ratio falls outside of the determined web tension ratio range the device and method alert a user to the fact that a condition leading to a web break is present and or control the measured web tensions to drive the calculated web tension ratio to be within the determined web tension ratio range 36 Claims 8 Drawing Sheets LOCATE PATH WITHIN INDUCTION TREE INDICATING THAT THE 749 PARTICULAR RESULT 1 MORE LIKELY TO OCCUR THAN ADD ELEMENT
23. alues associated with chosen attribute are then identified as nodes of the tree and are examined If all of the data records associated with a node are all of the same class the node is labeled as a leaf or endpoint of the induction tree Otherwise the node 1 labeled as a branching point of the induction tree The method then chooses a branching point calculates the information gain value for each of the remaining attributes based on the data from the records associated with the chosen branching point chooses the attribute with the highest information gain value and identifies the attribute values of the chosen attribute as nodes which are examined for leaves and branching points This process is repeated until only leaves remain within the induction tree or until at any existing branching point there are no attributes remaining upon which to branch After an induction tree is constructed classification rules are gener ated therefrom by tracing a path from a particular leaf of the induction tree to the root of the induction tree or vice versa As noted above choosing the appropriate variables or attributes for such an expert system is an important step in identifying the cause of a problem such as web breaks Without the appropriate choice of attributes the expert system can be practically useless in actually determining the causes of problems such as web breaks in a printing system SUMMARY OF THE INVENTION The present invention is d
24. ar result such as web breaks in a printing system that identifies web tension ratio ranges associated with the decreased occurrence of web breaks in a 6 009 421 11 printing system and or that prescribes and implements a solution that decreases the probability of occurrence of for example web breaks in a printing system Although the particular result described hereinafter e g a web break comprises an undesirable outcome of a process and the method is used to decrease the occurrence of the particular result the particular result could instead comprise a desir able outcome or other desirable effect associated with the process e g no web break and the method could be used to increase the probability that the particular result will occur At the start of the method step 32 a domain expert who is knowledgeable about a process specifies a particular result such as a web break associated with the process e g a printing system At a step 34 the domain expert defines classes associated with the particular result Typically the nonoccurrence of the particular result is associated with a first class and the occurrence of the particular result is associated with a second class At a step 36 the domain expert identifies attributes or features of the process that are potentially relevant to the occurrence of the particular result of the process These attributes can be continuous e g real valued or discrete If an attribu
25. common and recurring problem in large scale printing systems is the occurrence of web breaks which happen when the web tears while the web is being fed through the individual components of the printing system Upon the occurrence of a web break the printing system must be shut down the torn web must be dislodged from the individual components of the printing system and then the web must be re fed through the printing system all of which takes a considerable amount of time and results in wasted paper and ink Furthermore in some instances web breaks may result in damage to components of the printing system While web breaks are a common problem in the printing industry the reasons or conditions that lead to the occur rence of any particular web break vary a great deal In fact web breaks may be caused by different factors or by different combinations of factors at different times in the same printing system Generally web breaks are avoided by having a local expert such as a printing press foreman oversee the press conditions and make suggestions for changes based mainly on past experiences with web breaks trial and error and general rules of thumb While some of these approaches are successful in decreasing the incidence of web breaks in the short term web breaks usually reappear later with very little indication as to the real cause of the reappearance Furthermore while local printing experts are usually capable of determining the g
26. d or printing attributes and the occurrence of web breaks Likewise this same decision tree induction analysis can be used to determine web tension ratio ranges that result in the decrease of web breaks during printing runs of a printing system Referring now to FIG 2 a system 20 that constructs induction trees for the purpose of identifying conditions leading to a particular result e g web breaks in a multi variant system includes a computer 21 which may be any type of processor having a memory 22 therein The com puter 21 which may be integral with or a part of the controller 17 of FIG 1 is connected to a display device 23 such as a CRT and to a data storage device 24 which stores data used by the computer 21 An input device such as a keyboard 25 allows a user to enter data and otherwise interact with the computer 21 If desired the storage device 24 may comprise a disk drive that alternatively or addition ally allows a user to input data into the computer 21 A printing device 26 is attached to the computer 21 and is capable of printing induction trees developed by the com puter 21 and or other information such as alarms generated by the computer 21 Other input output devices might alter natively or additionally be used Referring now to FIGS 3A and 3B a flowchart illustrates a method that may be implemented in part by programming executed by the computer 21 FIG 2 that identifies condi tions leading to a particul
27. e vg Total number of value groups associated with attribute Number of records within the current database that are associated with the value group i of attribute A and that are associated with the first class Number of records within the current database that are associated with the value group i of attribute A and that are associated with the second class and n calculated for p p and n nj Although the information gain value IG A is useful it is biased toward those attributes that have a greater total number of value groups Thus an attribute having two value groups each with an equal probability of having a particular class associated therewith will have an information gain value that is less than the information gain value of an attribute having six value groups each with an equal prob ability of having a particular class associated therewith To correct this bias the following normalizing information gain value NG A for attribute A is calculated by the block 108 IG A 4 noa 160 4 NF A where S i i ni ni 5 NF A X log log pi ni Pithi pithi Pi tni i 1 Next a block 110 determines if any of the attributes active within the context heuristics have positive normalized infor mation gain values If none of the attributes has a positive normalized information gain value the block 110 terminates further branching from the node and control passes to the
28. e data set 1 are associated with the first class than are associated with the second class Likewise it is assumed that the second class is associated with the data set S2 such that proportionately more of the values within the data set S2 are associated with the second class than are associated with the first class If however the class flag indicates that the data sets 51 and S2 have been relabeled during the discretization process it is assumed that the first class is associated with the data set S2 and that the second class is associated with the data set S1 With respect to real valued attributes the block 152 determines if the class associated with the data set S1 or S2 as defined by the class flag is consistent with the class heuristic If so the distribution of classes is said to be consistent with the class heuristic wherein the latter indi cates whether higher or lower values of an attribute are expected to be associated with one of the classes A class associated with the data set S1 is consistent with a class heuristic that indicates that lower values of the attribute are more likely to be associated with the class than higher values Likewise a class associated with the data set S2 is consistent with a class heuristic that indicates that higher values of the attribute are more likely to be associated with the class than lower values of the attribute Preferably for discrete valued attributes a class heuristic indicates a va
29. e using for example the key board 25 of FIG 2 or another input device If a node termination condition exists the block 104 terminates branching from the node and a block 105 deter mines if any unexamined nodes remain If no unexamined nodes remain the induction tree is complete and the pro gram ends If however all of the nodes have not been examined a block 106 locates the next node updates the current database to be that associated with the next node and returns control to the block 102 Alternatively the block 106 can allow a user to select the next node to examine If the block 104 does not find a termination condition a block 107 places each of the attributes in the characteriza tion list into a context set identified for that node The context set at each node is used to determine if an attribute 6 009 421 15 15 active within the context heuristics The context set for a particular node other than the root includes 1 the context set for the node from which the particular node branched this node hereinafter referred to as the previous node 2 any attribute identified in the characterization list by the block 102 for the particular node and 3 the attribute chosen as the branch from the previous node to the particular node The context set for the root of the induction tree contains only those attributes identified in the characteriza tion list at the root of the induction tree The block 107 then partiti
30. each of which is uniquely identified with for example an identification number Thus as will be understood from the above each for example paper web used by a printing system has a mill site paper making machine a manufacturing date a reel set a log position and particularly identified auxiliary paper machines associated with the manufacture thereof Furthermore each web has a tensile strength a moisture content and a coefficient of friction associated therewith which are determined by the particular manufacturing pro cesses and machines used In particular the coefficient of friction indicates the friction between adjacent layers of the web as the web is unrolled from a reel The tensile strength the moisture content and the coefficient of friction of each web may be measured by the web manufacturer or by the web purchaser in any desired manner Generally speaking any one or more of these web manu facturing attributes may be correlated with the occurrence of web breaks or other problems in the web during printing thereon For example one of the web manufacturing machines or one of the auxiliary paper machines may have a fault therein that creates weakness in a web at certain locations These weaknesses which may be otherwise undetectable may lead to web breaks during printing Likewise a particular web moisture content may lead to web breaks under some conditions that would not otherwise result in web breaks Of course th
31. eb breaks in any other types of printing systems including for example those which print on fabric webs wallpaper webs linoleum webs sheet metal webs etc Likewise the same attributes and methods described herein may be used to identify the causes of and to reduce the occurrence of other problems within a printing system including for example web defects Numerous modifications and alternative embodiments of the invention will be apparent to those skilled in the art in view of the foregoing description Accordingly this descrip tion is to be construed as illustrative only and not as limiting to the scope of the invention The details of the structure may be varied substantially without departing from the spirit of the invention and the exclusive use of all modifications which are within the scope of the appended claims is reserved It is claimed 1 A device for determining web tension conditions lead ing to web breaks in a printing system comprising a database that stores data indicating a tension ratio for a plurality of printing runs wherein the tension ratio specifies a ratio of a first web tension at a first location within the printing system to a second web tension at a second location within the printing system and wherein a web break occurred during a portion of the plurality of printing runs and means for determining from the stored data a value of the tension ratio for which a web break is less likely to occur dur
32. eb provided by a web manufacturer and to decode that bar code number according to the proprietary numbering or identifying scheme used by the web manufacturer Any general or standard bar code reading and decoding software or circuitry may be used to perform this function as long as the numbering or identify ing scheme of the web manufacturer is known However the web manufacturing attribute data may be determined in any other manner including for example by being supplied directly from the web manufacturer After the web manufacturing attribute data is collected for a plurality of printing runs e g one printing run for each web and an indication of whether those runs resulted in a web break or no web break is determined any standard 10 15 30 35 40 45 50 55 60 65 10 correlation analysis such as a neural network analysis an expert system analysis a fuzzy logic analysis etc may be applied to the stored data to determine if there is a correla tion between any one or more of the web manufacturing attributes and the occurrence of web breaks or other prob lems within the printing system Of course if desired the correlation analysis may be performed using both web manufacturing attribute data and printing attribute data such as the web tension data dis cussed above to determine if a correlation between any combination of these attributes results in an increased or decreased occurrence of web breaks
33. eneral cause of any particular web break after the web break has occurred and moreover are generally capable of altering press conditions to eliminate a particular cause of a web break in the short term there is no guarantee that the altered conditions will not result in further web breaks for other reasons or that the press conditions suggested by the local expert will be implemented in the press for a long period of time It is generally known that one of the most common conditions leading to the occurrence of a web break is excessive tension within the web at one or more locations within the printing system Generally speaking a discrete amount of tension must be present PATENT in the web to assure that the different printing cylinders of the printing system begin to register on the web at the same location Slack within the web may cause misalignment between the different images which in turn produces an inferior product 10 15 20 25 30 35 40 45 50 55 60 65 2 However as noted above too much tension at any particular location leads to web breaks Unfortunately even with this rule of thumb it is not generally known what the tension at any particular web location should be to decrease the likelihood of web breaks or for that matter why some tensions are better than others Recently it has been suggested to use an expert system to determine the causes of problems such as web breaks within a p
34. ension in the web 12 at these locations In particular the reel 11 applies a reel tension to the web 12 as it leaves the reel 11 the infeed roller 13 applies an infeed tension to the web 12 passing thereover the drag roller 14 applies a drag tension to the web 12 and each of the ribbon rollers 15 applies a ribbon tension to a portion of the web 12 As is known load cells may be located on each of the reel 11 the infeed roller 13 the drag roller 14 and the ribbon rollers 15 to measure the tension on the web 12 at these locations Of course such load cells usually measure the force applied to the web 12 in for example pounds which can be converted to pounds per linear inch PLI based on the width of the web or ribbon passing over the rollers 11 13 14 and 15 If desired however other tension measurement devices such as pressure transducers may be used to measure the tension in the web 12 at these or at any other locations While it is common practice to adjust the reel infeed drag and or ribbon tensions in the web 12 to control the printing system 5 in an effort to prevent the occurrence of web breaks within the web 12 there has been before now no standard ized approach to adjusting these or other web tensions together to reduce web breaks Thus as noted above while adjusting one or more of the above identified web tensions may alleviate web breaks for a short period of time web breaks tend to reoccur with no apparent explana
35. ension of the web to keep for example one or more of the infeed to reel tension ratio the drag to infeed tension ratio and or the ribbon to drag tension ratio within respective predetermined ranges The controller may also alert a user when one or more of the calculated web tension ratios falls outside of the predeter mined ranges According to a still further aspect of the present invention a device for use in reducing web breaks in a printing system includes a first tension measurement device that measures a first web tension at a first location within the printing system a second tension measurement device that measures second web tension at a second location within the printing system and a processor that determines a web tension ratio of the first web tension to the second web tension A comparator then compares the determined web tension ratio to a predetermined tension ratio range and an indicating device such as an alarm indicates when the determined web tension ratio is outside of the predetermined tension ratio range In the case in which the first location is a drag device and the second location is an infeed device the processor determines the drag to infeed tension ratio and the compara tor may compare the determined drag to infeed tension ratio to a predetermined tension ratio range between about 0 85 and about 1 15 and or about 1 0 BRIEF DESCRIPTION OF THE DRAWINGS FIG 1 is a partial block and partial schematic dia
36. ermined value s or back within its their respective predetermined range s In this manner the controller 17 operates to reduce the occurrence of future web breaks based on one or more calculated tension ratios determined from actually measured web tensions It has been found particularly desirable to determine and control one or more of the ratio of the infeed tension to the reel tension the ratio of the drag tension to the infeed tension and or the ratio of the ribbon tension to the drag tension to reduce the occurrence of web breaks within the printing system 5 Furthermore in a particular embodiment it has been discovered advantageous to keep the ratio of the infeed tension to the reel tension of the printing system 5 at approximately within the range of 1 0 or alternatively within the range of 0 85 to 1 15 Of course this range may change depending upon the type of web being used and the type of printing system being controlled as well as other factors specific to the individual printing system web com bination of course tension ratios other than those identified herein may be used as well As will be understood the particular tension ratio that leads to reduced web breaks within the printing system 5 may differ for different printing systems and may in fact differ for different conditions within any individual printing system because for example different types of web mate rials are used within that printing system As a
37. ert pp 41 47 1993 Quinlan C4 5 Programs for Machine Learning 1993 Kerber ChiMerge Discretization of Numeric Attributes Proceedings of the Tenth National Conference on Artificial Intelligence pp 123 127 1992 Porter et al Concept Learning and Heuristic Classification in Weak Theory Domains 45 Artificial Intelligence pp 229 263 1990 Evans An Application of ID3 Using Real Valued Features 1991 Evans et al Overcoming Process Delays with Decision Tree Induction IEEE Expert pp 60 66 1994 Brochure by Integral Solutions Limited ZSL Rules A Powerful Tool for Generating Embeddable Rules 1989 Letter to applicant from Integral Solutions Limited dated Apr 27 1992 Letter to applicant from Infolink Decision Services Limited dated Jan 27 1994 6 009 421 3 Integral Solutions Limited Catalyst Catalog p 47 1991 Wang et al A Brain Tumor Diagnostic System with Auto matic Learning Abilities Proceedings of Third Annual IEEE Symposium on Computer Based Medical Systems pp 313 320 Jun 1990 Simpson W R Sheppard J W The Multicriterion Nature of Diagnosis Proceedings IEEE Systems Readiness Tech nology Conference pp 389 395 Sep 20 1993 Hunt K J Induction of Decision Trees for Rule Based Modelling and Control International Symposium on Intel ligent Control 1992 Proceedings of the 1992 IEEE pp 306 311 Aug 11 1992 Love Daniel J Failure Analysis
38. ese or other web manu facturing attributes may be correlated with web breaks or other problems for any number of other reasons It has been determined therefore that it is advantageous to collect data pertaining to web manufacturing attributes during a plurality of printing runs in which web breaks occur and do not occur and thereafter to run a correlation analysis such as an expert system analysis or other type of analysis on that data to determine if any of the web manufacturing attributes are correlated with web breaks or other problems within the printing system In this manner web manufacturing attributes may be useful in identifying conditions under which web breaks are more likely to occur in a printing system In one embodiment a database is used to store data associated with one or more of the mill site the web manufacturing machine the date the reel number the reel set the log position the auxiliary manufacturing equipment the tensile strength the moisture content and or the coeffi cient of friction of each web used in a plurality of printing runs along with an indication of whether a web break and or other problem occurred during each of the plurality of printing runs Typically some or all of this web manufac turing information is encoded on each web or roll of paper provided from a paper manufacturer in the form of a bar code number To obtain this information it may be necessary to scan the bar code of each w
39. f the new nodes for examination defines a new current database for each selected node and places the selected attribute into the context set for that node The new current database includes all of the records within the database of the previous node having values associated with the value group of the new node When one of the nodes has been selected the block 116 stores an indication of the other nodes that were created by the block 114 and an indication of the databases and the context sets associated with those nodes for future examination in for example the data storage unit 24 of FIG 2 The block 116 then returns to the block 102 which begins an iteration for the new node Referring now to FIGS 5A and 5B the operation of the block 107 of FIG 4 will be described in detail A block 122 selects a present attribute and determines whether the present attribute is active within the context heuristics In doing so the block 122 compares the context set for the node with a context list associated with the present attribute The context list associated with the present attribute identifies those attributes that must be branched upon in the induction tree before the present attribute can become active If all of the attributes within the context list associated with the 10 15 20 25 30 35 40 45 50 55 60 65 18 present attribute are also within the context set of the node being examined the present attribute i
40. gram of a printing system having a controller according to the present invention therein FIG 2 is a block diagram of a system for use in building an induction tree according to the present invention FIGS and 3B when joined along similarly lettered lines together form a flowchart of steps undertaken during a method of identifying conditions leading to a web break according to the present invention FIG 4 is a flowchart of programming executed by the system of FIG 2 for implementing a portion of the method identified by the flowchart of FIGS 3A and 3B FIGS 5A and 5B when joined along similarly lettered lines together form a flowchart of programming for imple menting a block of FIG 4 and FIG 6 is an induction tree constructed according to the present invention for identifying conditions leading to web breaks in a rotogravure printing process DESCRIPTION OF THE PREFERRED EMBODIMENT Referring now to FIG 1 a standard printing system 5 which may be a rotogravure printing press includes a reel support 6 various printing stations 7 and 8 a ribbon cutter 9 and a folder cutter 10 A reel 11 having a web 12 made of for example paper is mounted on the reel support 6 and is driven to feed the web 12 over an infeed roller 13 through the printing stations 7 which print an image on one side of 10 15 20 25 30 35 40 45 50 55 60 65 6 the web 12 back over the printing stations 7 and the
41. he outcome of the process is not desirable or if the outcome of the process returns to an undesirable condition during further monitor ing of the process the method returns to the step 46 of FIG 3A at which the expert builds a new induction tree collects additional data for the identified attributes changes heuris tics or identifies new attributes all in an effort to generate a more complete or accurate solution that is to identify better web tension ratio values or ranges and or to identify other correlations between web manufacturing attributes and web breaks or other web problems Generally the induction tree constructed at the step 42 has root and any number of nodes that branch from either the root or from another node of the induction tree The induc tion tree is constructed iteratively and performs the same operations at the root and each node using only data con tained in records that are in a current database that has a content that varies with the position in the induction tree At the root of the induction tree the current database includes all of the records produced at the steps 40 and 60 The current database associated with any particular node of the induction tree includes a subset of the records of the data base associated with the node or root from which the particular node branches FIG 4 illustrates a flowchart of programming preferably in LISP a commercially available programming language particula
42. he ratio of the first and second tensions and an alarm that notifies a user when the ratio of the first and second tensions falls outside the predetermined range 16 The controller of claim 10 wherein the predetermined range comprises a predetermined value 17 A device for determining conditions leading to a break of a printing web in a printing system comprising database that stores data indicating a tension of the printing web at a first location within the printing 5 10 15 20 25 30 35 40 45 50 55 60 65 24 system for a plurality of printing runs wherein a web break occurred during a portion of the plurality of printing runs and means for implementing a decision tree induction learn ing algorithm that uses the stored tension data to determine a first tension value at the first location for which a web break is less likely to occur 18 The device of claim 17 wherein the printing system includes an infeed device that creates an infeed tension a reel device that creates a reel tension a drag device that creates a drag tension and a ribbon device that creates a ribbon tension and wherein the implementing means includes means for determining the tension value for one of the infeed tension the reel tension the drag tension and the ribbon tension 19 The device of claim 17 wherein the database stores further data indicating a tension of the web at a second location in the printing system
43. he reel device to keep a ratio of the infeed tension to the reel tension within the predetermined range 12 The controller of claim 10 wherein the tension detector includes a first tension measurement device coupled to a drag device of the printing system to detect a drag tension and a second tension measurement device coupled to an infeed device of the printing system to detect an infeed tension and wherein the controlling means controls one of the drag device and the infeed device to keep a ratio of the drag tension to the infeed tension within the predetermined range 13 The controller of claim 10 wherein the tension detector includes a first tension measurement device coupled to a ribbon device of the printing system to detect a ribbon tension and a second tension measurement device coupled to drag device of the printing system to detect a drag tension and wherein the controlling means controls one of the ribbon device and the drag device to keep a ratio of the ribbon tension to the drag tension within the predetermined range 14 The controller of claim 10 wherein the controlling means includes means for computing a ratio of the first and second tensions and means coupled to the printing system for causing a change in one of the first and second tensions to force the ratio of the first and second tensions to be within the predetermined range 15 The controller of claim 10 wherein the controlling means includes means for computing t
44. hine Learning pp 81 92 1991 Padalkar et al Real Time Fault Diagnostics IEEE Expert 75 85 Jun 1991 Quinlan Induction of Decision Trees 1 Machine Learning pp 81 106 1986 Shavlik et al Symbolic and Neural Learning Algorithms An Experimental Comparison 6 Machine Learning pp 111 143 1991 Smith et al Categories and Concepts Chs 1 2 8 pp 1 21 162 182 1981 Fisher Knowledge Acquisition Via Incremental Conceptual Clustering 2 Machine Learning pp 139 172 1987 Rich Artificial Intelligence 1983 Mingers An Empirical Comparison of Pruning Methods for Decision Tree Induction 4 Machine Learning pp 227 243 1989 Whitehall et al CAO A Machine Learning Tool for Engi neering vol 5 No 4 Artificial Intelligence in Engineering pp 189 198 1990 Schlimmer Incremental Adjustment of Representations for Learning Proceedings of the Fourth International Workshop on Machine Learning 79 90 1987 Lu et al A Machine Learning Approach to the Automatic Synthesis of Mechanistic Knowledge for Engineering Deci sion Making 1 AI EDAM pp 109 118 1987 Buntine et al Interactive Induction 12 Machine Intelli gence 121 137 Hayes Michie et al eds 1990 Clark et al Using Qualitative Models to Guide Inductive Learning Proceedings of the Tenth International Machine Learning Conference pp 49 56 1993 Irani et al Applying Machine Learning to Semiconductor Manufacturing IEEE Exp
45. identifying attributes that are not actually relevant to the occurrence of the particular result will not degrade the performance of the method or the solution ultimately obtained thereby At a step 38 the domain expert may identify class and context heuristics or rules associated with the attributes identified at the step 36 A class heuristic represents a known relationship between the distribution of classes and specific portions of the range of an attribute A class heuristic preferably specifies that a particular range of an attribute should include a higher or lower proportion of attribute values that are associated with a particular one of the classes than any other range of the attribute Class heuristics are used to prevent the method from searching for induction rules that are already known to be inaccurate in connection with the domain or the process Acontext heuristic represents an order of priority between two or more attributes A context heuristic may for example 10 15 20 35 40 45 50 55 60 65 12 specify that it is meaningless to search for induction rules associated with one of the identified attributes before search ing for induction rules associated with a different one of the attributes Thus it may not make sense to search for an induction rule associated with a paper manufacturing machine before searching for one associated with a mill site The attribute with the lower priority is said t
46. im 1 wherein the determining means comprises a decision tree induction learning algorithm that creates an induction tree based on the stored data 8 The device of claim 1 further including an output device that produces an indication of the determined tension ratio value 9 The device of claim 1 wherein the tension ratio value comprises a tension ratio range and further including a controller coupled to the printing system that controls the printing system to keep the tension ratio of the first web tension at the first location to the second web tension at the second location within the tension ratio range 10 A controller for controlling a printing system having printing web moving therethrough comprising a tension detector coupled to the printing system to detect a first tension of the printing web at a first location and to detect a second tension of the printing web at a second location and means for controlling the printing system to keep a ratio of the first tension to the second tension within a predetermined range to reduce breaks within the print ing web 11 The controller of claim 10 wherein the tension detector includes a first tension measurement device coupled to an infeed device of the printing system to detect an infeed tension and a second tension measurement device coupled to a reel device of the printing system to detect a reel tension and wherein the controlling means controls one of the infeed device and t
47. ing another run of the printing system 2 The device of claim 1 wherein the database stores data indicating web tensions at the first and second locations for the plurality of printing runs 3 The device of claim 1 wherein the printing system includes an infeed device that creates an infeed tension and a reel device that creates a reel tension and wherein the tension ratio comprises the ratio of the infeed tension to the reel tension 4 The device of claim 1 wherein the printing system includes a drag device that creates a drag tension and an infeed device that creates an infeed tension and wherein the tension ratio comprises the ratio of the drag tension to the infeed tension 5 The device of claim 1 wherein the printing system includes a ribbon device that creates a ribbon tension and a drag device that creates a drag tension and wherein the tension ratio comprises the ratio of the ribbon tension to the drag tension 6 The device of claim 1 wherein the printing system includes an infeed device that creates an infeed tension a reel device that creates a reel tension a ribbon device that creates a ribbon tension and a drag device that creates a drag tension and wherein the tension ratio comprises the ratio of 6 009 421 23 one of the infeed tension the reel tension the ribbon tension and the drag tension to another one of the infeed tension the reel tension the ribbon tension and the drag tension 7 The device of cla
48. irected to a device and a method that identify conditions leading to web breaks within a printing system based on web tension ratios The device and method may alert a user to the fact that a condition that is likely to result in a web break exists and or may automati cally control the printing system to prevent or eliminate a condition that is likely to result in a web break In particular the device and method determine a ratio of web tensions at different locations within the printing system to determine if the web tension ratio does not match a predetermined value or is outside of a predetermined range identified as a tension ratio value or range at which web breaks are less likely to occur The device and method of the present invention also determine a web tension ratio value and or a web tension ratio range for which web breaks are less likely to occur within the printing system based on data collected from a plurality of previous runs of the printing system According to one aspect of the present invention a device and a method for determining web tension conditions lead ing to a web break in a printing system store data indicating a tension ratio for a plurality of printing runs and determines from the stored data a value of the web tension ratio for which a web break 1 less likely to occur during another run of the printing system Preferably the web tension ratio specifies a ratio of a first web tension at a first location within
49. lue or a value group of the attribute and the class that should be predominantly associated with that value group Thus for discrete valued attributes the block 152 determines whether there is a higher or lower percent age of a class within the value group defined by the class heuristic than the percentage of that class in any other range of the attribute For example if the class heuristic identifies that one value group is more likely to be associated with the first class the block 152 compares the percentage of values in the one value group that are associated with the first class to the percentage of the values of that attribute associated with the first class in each of the other value groups If the percentage of values associated with the first class is highest in the one value group the distribution of classes among the value groups is consistent with the class heuristic If the block 152 determines that the distribution of classes predominantly associated with the value groups of the attribute Is inconsistent with the class heuristic identified for the attribute a block 154 marks the attribute with a dis agreement flag After the attribute has been marked by the block 154 or if the block 152 does not detect an inconsistency between the distribution of the classes of the values within the value groups of the attribute and a class heuristic defined for the attribute the block 156 of FIG 5A determines if all of the attributes that
50. ly discriminate the classes Information gain values between 0 and 1 indi cate less than complete correlation between the value groups and the classes i e there is some distribution of classes among the value groups of the attribute Information gain values close to 1 indicate a high correlation between the attribute value groups and the classes and information gain values close to 0 indicate a low correlation between the attribute value groups and the classes An information gain value of 0 indicates that no correlation between the attribute value groups and the classes exists and thus that the classes are randomly distributed throughout the value groups of the attribute In such a case the distribution of the classes is not affected by the selection of the attribute and so selection of the attribute at the node would not be particu larly helpful Preferably the information gain value IG A of an attribute A is calculated as follows IG A n E A a wherein n n Kp n log los m ptn pen PER 10 15 20 25 30 35 40 45 50 55 60 65 16 continued E A Expected value of attribute A 9 vg l where Pi ni p n ni p Number of records within the current database associated with the first class and Number of records within the current database associated with the second class and wher
51. natively or additionally used Preferably any attribute that has been marked by the block 107 as having a distribution of classes among its value groups that is inconsistent with a class heuristic is identified as such by for example placing brackets around the dis played normalized information gain value of that attribute Alternatively the normalized information gain value of any such attribute can be set to zero The block 112 then permits selection of one of the attributes as a branch within the induction tree Preferably the block 112 allows the domain expert to interactively select one of the attributes that also preferably has a positive normalized information gain value It is important to note however that the expert need not select the attribute having the highest normalized information gain value but can select any of the attributes active within the context heuristics according to any desired criteria Alternatively the block 112 can automatically select one of the attributes and in such a case preferably selects the attribute with the highest normalized information gain value However auto matic selection of an attribute may lead to a less complete or desirable solution Ablock 114 causes branching on the chosen attribute such that new nodes are created within the induction tree each of which corresponds to a value group of the chosen attribute A block 116 permits a user to interactively terminate or to select each o
52. nd an indication of the class of the object An expert then reviews the induced rules to identify which rules are most useful or applicable to the classification task being performed Such a system is disclosed in the above identified parent application This method has the advantage of using the expert in a way that the expert is accustomed to working that is identifying whether particu lar rules are relevant or useful in the classification task It should be noted however that all of the relevant attributes of the objects being classified must be identified and data for those attributes must be provided within the records in order for the system to induce accurate and complete classification rules A classic example of a pure machine induction technique is described in an article by J R Quinlan Induction of Decision Trees 1 Machine Learning 81 106 1986 the disclosure of which is hereby incorporated by reference herein This technique searches through relations between combinations of attribute values and classes of objects to build an induction tree which is then used to generate precise classification rules During operation the Quinlan method calculates a statistical measurement referred to as an infor mation gain value for each of a set of attributes and chooses the attribute with the highest information gain value at a root 10 15 20 25 30 35 40 45 50 55 60 65 4 of the tree The attribute v
53. o be inactive within the context heuristics until the method has examined the attribute with the higher priority At a step 40 data or values are collected for each of the attributes for each of a number of runs of the process This data may include values for the web manufacturing and web tension attributes identified above A plurality of data records are then created each of which includes values for the attributes identified at the step 36 along with the class associated with a particular run of the process The plurality of records comprises a database that is used to develop induction rules associated with the process and that is stored within for example the storage device 24 of FIG 2 preferably in text format It is important that the values for the attributes are measured accurately Inaccurate and or incomplete data may lead to an inaccurate determination of the cause of the particular result or may lead to an inaccurate solution for increasing or decreasing the probability of the occurrence of the particular result As a result data prepro cessing that for example replaces outliners clearly inac curate data fills in missing data eliminates records having incorrect or missing data etc may be performed to purify the data At a step 42 the records created at the step 40 are used to construct an induction tree Preferably at the step 42 the domain expert is allowed to guide the construction of the induction tree interac
54. of the web 12 The controller 17 then calculates the ratio of two measured tensions and compares this calculated ratio to a predetermined value or range to deter mine if the calculated ratio is at the predetermined value or within the predetermined range If the calculated ratio is not at the predetermined value or within the predetermined range the controller 17 may alert a user using an appropriate alarm such as a bell a whistle a display device such as a CRT a flashing light etc or any other display or indicating unit to indicate that either one or both of the measured tensions should be adjusted to force the tension ratio back to the predetermined value or within the predetermined range Of course the controller 17 may measure any number of different tensions may calculate any desired number of different tension ratios based on those measurements may compare those tension ratios with different respective ratio values or ranges and may alert a user when one or more of the calculated ratios falls outside of a predetermined value or range If desired the controller 17 may be connected to for example tension control power sources for the reel 11 the infeed roller 13 the drag roller 14 the ribbon rollers 15 or any other tension control devices within the printing system 5 to automatically increase or decrease the tension at one or more of the web locations to force the determined tension ratio s back to its their respective predet
55. of using a decision tree induction learning technique 25 The method of claim 21 wherein the step of collecting includes the step of ascertaining data pertaining to an infeed tension at the first location and a reel tension at the second location and wherein the web tension ratio comprises the infeed tension to the reel tension ratio 26 The method of claim 21 wherein the step of collecting includes the step of ascertaining data pertaining to a drag tension at the first location and an infeed tension at the second location and wherein the web tension ratio comprises the drag tension to the infeed tension ratio 27 The method of claim 21 wherein the step of collecting includes the step of ascertaining data pertaining to a ribbon tension at the first location and a drag tension at the second location and wherein the web tension ratio comprises the ribbon tension to the drag tension ratio 28 The method of claim 21 wherein the step of collecting includes the steps of ascertaining data pertaining to one of an infeed tension a reel tension a drag tension and a ribbon tension at the first location and ascertaining data pertaining to another one of the infeed tension the reel tension the drag tension and the ribbon tension at the second location and 6 009 421 25 wherein the web tension ratio comprises the ratio of the one of the infeed tension the reel tension the drag tension and the ribbon tension to the another one of the infeed
56. ons each active attribute into a finite number of value groups Discrete attributes are parti tioned into value groups according to discrete categories associated therewith Real valued or continuous attributes are partitioned into value groups based on the actual values of that attribute within the current database and the classes associated with those values as described hereinafter with respect to FIGS 5A and 5B The block 107 may also determine whether the actual distribution of the classes among the value groups is consistent with the class heuris tics defined for the attributes If the block 107 discovers an inconsistency between the actual distribution of the classes among the value groups of an attribute and the distribution specified in the class heuristic that attribute is marked with disagreement flag Next a block 108 calculates a figure of merit such as the normalized information gain value for each of the attributes active within the context heuristics using the value groups developed by the block 107 The information gain value of an attribute is a measure of the distribution of the classes across the value groups of the attribute The information gain value is defined such that a value of 1 indicates a complete or perfect correlation between the attribute value groups and the classes In such a case each attribute value group contains instances of only one class or is an empty set and hence the value groups complete
57. osis are presented to the system for clas sification within one of a predetermined number of classes The system compares training example with one or more exemplars stored for each of the classes and uses a set of classification rules developed by the system to determine the class to which the training example most likely belongs A domain expert such as a doctor either verifies the classifi 6 009 421 3 cation choice instructs the system that the chosen classi fication is incorrect In the latter case the expert identifies the correct classification choice and the relevant attributes or values thereof that distinguish the training example from the class initially chosen by the system The system builds the classification rules from this information or if no rules can be identified stores the misclassified training example as an exemplar of the correct class This process is repeated for training examples until the system is capable of correctly classifying a predetermined percentage of new examples using the stored exemplars and the developed classification rules A patent to Karis U S Pat No 5 521 844 discloses a case based expert system that may be used to aid in the identification of the cause of a particular problem such as a web break in a printing system The expert system disclosed in the Karis patent stores data related to a set of previous printing runs or cases in which the problem e g a web break act
58. positions through 7 As illustrated by the summary block 238 the log position includes zero records associated with the NO WB class and 5 records associated with the WB class and is therefore an endpoint of the induction tree 200 The endpoint 234 suggests that there is a strong correlation between the log position and web breaks especially at infeed tensions greater than 1 88 PLI Thus based on the induction tree 200 it may be seen that drag tension to infeed tension ratio of approximately 1 within the range of 0 95 is appropriate for reducing the occurrence of web breaks in the printing system for which the web tension data was collected and that webs associated with the A log position of a particular mill site and paper making machine are highly correlated with web breaks Also using an infeed tension above 1 88 PLI may result in an increase in web breaks and this tension range should therefore be avoided Based on the above results the operator of or a controller e g the controller 17 of FIG 1 connected to the printing system for which the data was collected may keep the ratio of the drag tension to the infeed tension at approximately 0 95 to reduce the occurrence of web breaks Thus if the drag tension must be increased for some reason the infeed tension should also be increased to keep the ratio of these tensions at approximately 0 95 Likewise the printing system operator may
59. re of for example the ratio of the infeed tension to the reel tension the ratio of the drag tension to the infeed tension the ratio of the ribbon tension to the drag tension and or any other desired tension ratio Thereafter any desired method of identifying a proper tension ratio value or range that results in reduced web breaks based on the stored data may be used Such methods may include the use of for example a neural network any correlation analysis an expert system etc However a preferred method of identifying one or more proper tension ratio values or ranges that result in reduced web breaks uses a decision tree induction analysis and will be described below It has also been determined that web breaks may be correlated with attributes that are outside of the control of the printer including for example attributes associated with the manufacture of the web 12 Generally speaking each web is manufactured by a mill which identifies the com pany that manufactured and distributed the web at typically one of a number of mill sites associated with the mill The mill site indicates the geographic location e g city of the particular plant in which the web 12 was made Each mill site includes one or more web making machines for example paper making machines that produces or generates large reels of material e g paper which may be for example 70 inches in diameter and 300 inches wide Each web making machine may produce
60. rinting press In particular the above identified parent application on which this application relies for priority is directed to the use of a decision tree induction analysis that identifies conditions leading to a particular result such as web breaks within a printing system In general expert systems are used to mimic the tasks of an expert within a particular field of knowledge or domain or to generate a set of rules applicable within the domain In these applications expert systems must operate on objects associated with the domain which may be physical entities processes or even abstract ideas Objects are defined by a set of attributes or features the values of which uniquely characterize the object Object attributes may be discrete or continuous Typically each object within a domain also belongs to or is associated with one of a number of mutually exclusive classes having particular importance within the context of the domain Expert systems that classify objects from the values of the attributes for those objects must either develop or be provided with a set of classification rules that guide the system in the classification task Some expert systems use classification rules that are directly ascertained from a domain expert These systems require a knowledge engi neer to interact directly with a domain expert in an attempt to extract rules used by the expert in the performance of his or her classification task Unfortuna
61. rly suited for artificial intelligence applications that is executed by the computer 21 to implement the step 42 of FIG 3A The programming begins at a block 102 which reports a summary of the records within the current database to the user via for example the display 23 of FIG 2 Preferably this summary indicates the number of records within the current database that are associated with each of the classes identified at the step 34 of FIG 3A The summary also identifies whether all of the records within the current database have the same value for any particular attribute and provides a characterization list that identifies the attributes for which that condition is satisfied The summary may also list the values of one or more attributes and indicate the classes of the records having these values to provide the expert with more information about the records within the current database A block 104 then determines if a node termination con dition is present Preferably a node termination condition exists if at least a predetermined percentage of the records within the current database are associated with the same class in which case the node is labeled as an endpoint or a leaf of the induction tree A node termination condition may also exist if all of the attributes active within the context heuristics have been selected as a branch within a path from the node to the root of the tree Alternatively a user can manually terminate the nod
62. s deemed to be active If the present attribute has an empty context list it is always active within the context heuristics A block 124 then determines if the present attribute is real valued If not then the present attribute is a discrete valued attribute and a block 126 of FIG 5B partitions the present attribute into value groups based on the categories associ ated with the present attribute that have been previously defined by the domain expert If the block 124 determines that the present attribute is real valued a block 130 forms two data sets S1 and S2 from the values of the present attribute The data set S1 includes all of the values of the present attribute in records within the current database associated the first class The data set S2 includes all of the values of the present attribute in records within the current database associated with the second class A block 132 sorts all of the values within each of the data sets S1 and S2 in ascending order and a block 134 deter mines the medians M1 and M2 for the data sets S1 and S2 respectively A block 136 determines whether the medians 1 and 2 are equal and if so the present attribute cannot be partitioned Control is then passed to a block 156 and as a result the present attribute will only have one value group and the normalized information gain value associated there with will be zero If on the other hand the medians M1 and M2 are not equal to one another a block
63. te is discrete the domain expert must identify the discrete values or categories that a value of the attribute can assume As noted above for the case of web breaks these attributes may include web manufacturing attributes such as a mill site a web making machine a manufacturing date a reel number a reel set a log position one or more auxiliary web machines a web tensile strength a web moisture content and or a coefficient of friction as well as printing attributes such as web tensions e g reel tension infeed tension drag tension and ribbon tension and web tension ratios e g infeed tension to reel tension drag tension to infeed tension ribbon tension to drag tension etc Of course other web manufacturing attributes and or printing attributes may be used as well including for example ambient printing room conditions such as humidity temperature etc In order for the method to be ultimately successful in determining the cause of the particular result such as a web break or in prescribing a solution that increases or decreases the probability of the occurrence of the particular result it is important that all of the attributes that are actually relevant to the particular result be identified If attributes that are actually relevant to the particular result are not identified at the step 36 the method may fail to determine the cause of the particular result or may produce an incomplete or inaccurate solution However
64. tely this technique usually requires a lengthy interview process that can span many man hours of the expert s time Furthermore experts are not generally good at articulating classification rules that is expressing knowl edge at the right level of abstraction and degree of precision organizing knowledge and ensuring the consistency and completeness of the expressed knowledge As a result the rules that are identified may be incomplete while important rules may be overlooked Still further this technique assumes that an expert actually exists in the particular field of interest Even if an expert does exist the expert is usually one of a few and is therefore in high demand As a result the expert s time and consequently the rule extraction process can be quite expensive It is known to use artificial intelligence within expert systems for the purpose of generating classification rules applicable to a domain For example an article by Bruce W Porter et al Concept Learning and Heuristic Classification in Weak Theory Domains 45 Artificial Intelligence 229 263 1990 describes an exemplar based expert system for use in medical diagnosis which removes the knowledge engineer from the rule extraction process and in effect interviews the expert directly to determine relevant classification rules In this system training examples data sets that include values for each of a plurality of attributes generally relevant to medical diagn
65. tension the reel tension the drag tension and the ribbon tension 29 A device for use in reducing web breaks in a printing system comprising a first tension measurement device that measures a first web tension at a first location within the printing system second tension measurement device that measures a second web tension at a second location within the printing system means for determining a tension ratio of the first web tension to the second web tension a means for comparing the tension ratio to a predeter mined tension ratio range and means for indicating when the determined tension ratio is outside of the predetermined tension ratio range 30 The device of claim 29 further including a controller for controlling a tension at one of the first location and the second location to force the ratio of the first tension to the second tension to be within the predetermined tension ratio range 10 15 20 26 31 The device of claim 29 wherein the first location is a drag device and the second location is an infeed device and the determining means determines the drag to infeed tension ratio 32 The device of claim 31 wherein the predetermined tension ratio range is between about 0 85 and about 1 15 33 The device of claim 31 wherein the predetermined tension ratio range comprises the range of approximately 1 0 34 The device of claim 29 wherein the first location is an infeed device and the second location
66. tep 34 Any particular induction tree may suggest any number of paths that lead to one or more components of a solution which when used to control the process will affect the probability of the occurrence of the particular result Rather than identifying induction rules manually by iden tifying such paths the identification of induction rules can be performed automatically A book written by J R Quinlan C4 5 Programs for Machine Learning 1991 in 6 009 421 13 particular chapters 5 and 9 and the appendix thereof discloses a technique that automatically searches for and identifies induction rules within an induction tree At a step 50 the components of the paths identified at the step 48 are added to a solution list which may be stored in the memory 22 or the storage device 24 associated with the computer 21 of FIG 2 Typically different paths of either the same or different induction trees may identify different ranges of the same attribute as one of the solution components If these ranges are not mutually exclusive and where it is practical to do so the domain expert preferably adopts the range included in all of the paths as the ultimate solution compo nent At a step 52 the domain expert determines whether the solution as compiled in the solution list is satisfactory If the domain expert believes that the solution is not complete the method proceeds to the decision step 46 of FIG 3A At the step 46 the domain e
67. tes are not in fact relevant to the particular result If at least one new attribute is identified at the step 64 the method returns to the step 38 at which class and context heuristics for the new or already identified attributes are defined At the step 40 data for a new plurality of runs of the process are collected to produce records having data for all of the attributes including the newly identified attribute s When at the step 52 of FIG 3B the expert is satisfied with the solution obtained at the step 50 the solution is incorporated into the process by running the process at a step 70 so that the process attributes have values within the ranges specified by the solution For example the tensions within the printing system 5 of FIG 1 may be controlled to keep the ratio of these tensions at a tension ratio value or within a tension ratio range determined to be associated with reduced occurrence of web breaks At a step 72 the process is monitored during subsequent runs thereof and a determination is made at a step 74 whether the solution has been adequate in achieving a desired outcome that is eliminating or reducing the particular result e g web breaks from the process in an acceptable manner If the outcome of the process is desirable the method returns to the step 72 which continues to monitor the 5 10 15 20 25 30 35 40 45 50 55 60 65 14 outcome of the process If however t
68. th the WB class Likewise as illustrated by the summary block 220 the Z og position had 41 records associated with the NO WB class and 2 records associated with the WB class Under the 7 log position node 216 Drag Tension was selected as a branch 222 Two drag tensions ranges specifically less than 1 75 PLI node 224 and greater than or equal to 1 75 PLI node 226 were identified for the drag tension attribute As indicated by the summary block 228 there were 38 records associated with the NO WB class and zero records associated with the WB class for the node 224 which makes the node 224 an endpoint of the induction tree 200 The endpoint 224 indicates that using infeed tension below 1 84 PLI and drag tension below 1 75 results in the avoidance of web breaks Of course this observation also suggests that using a drag tension to infeed tension ratio of approximately 1 75 over 1 84 or 0 95 is likely to decrease the incidence of web breaks in the printing system for which the data was collected Under the node 208 which has 14 records associated with the NO WB class and 8 records associated with the WB class summary block 230 the log position attribute was 6 009 421 21 again chosen as a branch 232 For the sake of illustration the log position attribute was broken into two separate nodes 234 and 236 wherein the node 234 is associated with the A log position and the node 236 is associated with the rest of the log
69. the printing system to second web tension at a second location within the printing system and the device and method use an induction tree decision routine to determine the predetermined web tension ratio value or range The printing system may include a reel device that creates a reel tension on the web an infeed device that creates an infeed tension on the web a drag device that creates a drag tension on the web and one or more ribbon devices that create a ribbon tension on the web As a result the tension ratio may comprise one or more of ratio of the infeed tension to the reel tension the ratio of the drag tension to the infeed tension and or the ratio of the ribbon tension to the drag tension According to another aspect of the present invention a controller for controlling a printing system having a printing 6 009 421 5 web moving therethrough includes a tension detector coupled to the printing system that detects a first tension of the printing web at a first location and that detects a second tension of the printing web at a second location The controller also includes a processor that controls the printing system to keep the ratio of the first tension to the second tension within a predetermined range to thereby prevent ie reduce the occurrence of web breaks within the printing web If desired the controller may measure and control two or more of the reel tension the infeed tension the drag tension and or the ribbon t
70. tion or correlation to the specific web tensions being used It has been discovered however that while web breaks are not necessarily correlated to any specific web tension at any specific location within the web 12 web breaks are corre lated with the ratio of different web tensions at different locations within the printing system 5 In other words it is not only the values of the web tensions at particular web locations that are indicative of impending web breaks but additionally the ratio of two web tensions at different locations within the web 12 that indicates when a web break is more likely to occur within the printing system 5 As a result web breaks may be reduced in the printing system 5 by keeping one or more web tension ratios at one or more values or ranges that have been predetermined as values or ranges at which web breaks are less likely to occur within the printing system 5 6 009 421 7 To reduce web breaks based on web tension ratios a controller 17 which may be any standard printing system controller including for example any analog or digital or hardwired processor or microprocessor is connected to the printing system 5 The controller 17 may be coupled to two or more of the load cells or other tension measurement devices at for example the reel 11 the infeed roller 13 the drag roller 14 the ribbon rollers 15 etc to measure the reel tension the infeed tension the drag tension the ribbon tension etc
71. tively Each induction tree created at the step 42 indicates relationships between values of the attributes and the classes identified for the process e g whether a web break or no web break occurred An indi cation of the induction tree may be provided to a user via for example the printing device 25 or the display device 23 of FIG 2 At a step 44 the domain expert reviews the induction tree to determine whether the induction tree is satisfactory i e whether any potentially relevant induction rules may be suggested thereby If the induction tree is not satisfactory because for example no induction rules can be identified or the induction rules that are identified are not implementable in the process due to economic social quality or other reasons the method proceeds to a decision step 46 However if the induction tree is satisfactory the method proceeds to a step 48 of FIG 3B at which the domain expert locates one or more paths within the induction tree that indicate that the particular result is more likely to occur than not Conversely or in addition the domain expert may also locate one or more paths within the induction tree that indicate that the particular result is less likely to occur than not Each path identified by the expert may comprise one or more attribute values or ranges of attribute values associated with runs of the process that fall exclusively or almost exclusively into one of the classes defined at the s
72. ually occurred An expert then goes through the cases and identifies the most likely reason or reasons that the problem occurred in each case These reasons are then stored in the memory of the expert system and thereafter the stored cases along with the cause and effect reasoning provided by the expert are used to classify the cause s of the problem when it arises later Unfortunately the Karis system requires the use of an expert to originally identify the most probable cause s of the problem and thus is totally depen dent on the expert s knowledge and reasoning The Karis system does not identify causes which were never identified by the expert because for example the expert did not connect the problem to a particular cause or because the cause did not result in the problem in one of the cases reviewed by the expert Furthermore the Karis system does not store or collect data pertaining to printing runs in which the problem did not occur As a result the Karis system cannot perform a data mining technique i e one in which causes are determined based on the data from both printing runs in which the problem did occur and printing runs in which the problem did not occur Other artificial intelligence methods that have been used in expert systems rely on machine induction in which a set of induction rules are developed or are induced directly from a set of records each of which includes values for a number of attributes of an object a
73. xpert chooses one of a number of options in order to improve the quality of the induction tree constructed at the step 42 and to enhance the solution compiled at the step 50 Following the step 46 a new induction tree may be built at the step 42 with further input from the domain expert Alternatively at the step 46 the method may proceed to a step 60 at which data is collected for additional runs of the process The resulting additional records are added to the database used at the step 42 to build an induction tree In this manner a more complete or informative induction tree can be constructed at the step 42 Also at the step 46 the method may proceed to a step 62 wherein the domain expert changes adds and or deletes one or more of the class and or context heuristics previously identified for the domain This step is particularly useful when an induction tree indicates that the class heuristics previously identified are incorrect Alternatively at the step 46 the method may proceed to a step 64 wherein the domain expert identifies additional attributes that may be relevant to the occurrence of the particular result but that were not previously identified This step is particularly useful when the induction tree developed at the step 42 does not present any clear results At the step 64 the domain expert can also delete attributes from the set of attributes previously identified when for example the expert believes that those attribu

Device and method for decreasing web breaks in a printing system

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