System and method for paper web time-to-break prediction

• US 6,542,852 B2
• Filed: 08/13/2001
• Issued: 04/01/2003
• Est. Priority Date: 09/15/1999
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Bonissone, Piero Patrone, Chen, Yu-To
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): Raymond, Edward
• Application Number:
  US09/929,535
• Publication Number:
  US 20020038197A1
• Time in Patent Office: 596 Days
• Field of Search: 702/182, 702/35, 702/36, 702/85, 702/115, 702/183, 702/184, 762/263, 735/98, 736/06, 736/49, 737/67, 737/94, 738/658, 344/45, 700/78, 700/79, 700/80, 700/83
• US Class Current: 702/182
• CPC Class Codes: D21F 7/04 Paper-break control devices
• CPC Class Codes: D21G 9/0009 Paper-making control systems

Abstract

System and method for generating a time-to-break prediction for a paper web in a paper machine. This invention uses principal components analysis, neuro-fuzzy systems and trending analysis to form a model for predicting the time-to-break of the paper web from paper mill measurements of paper machine process variables. The model is used to isolate the root cause of the predicted web break.

Claims

1. A system for predicting a paper web break in paper machine located about a paper mill, comprising:

a paper mill database containing a plurality of measurements obtained from the paper mill, each of the plurality of measurements relating to a predetermined paper machine variable;

a processor for processing each of the plurality of measurements into modified break sensitivity data; and

a break predictor responsive to the processor for predicting a time-to-break of the paper web from the plurality of processed measurements.

2. The system according to claim 1, wherein the break predictor comprises a predictive model.

3. The system according to claim 2, wherein the predictive model comprises a neuro-fuzzy system.

4. The system according to claim 2, wherein the predictive model comprises an adaptive network-based fuzzy inference system.

5. The system according to claim 4, wherein the adaptive network-based fuzzy inference system is trained with historical web break data.

6. The system according to claim 1, wherein the modified break sensitivity data comprise time-based transformations of the plurality of measurements.

7. The system according to claim 1, wherein the modified break sensitivity data comprise principal components of the plurality of measurements.

8. The system according to claim 1, wherein the break sensitivity data comprise noise-reduced and feature-enhanced transformations of the plurality of measurements.

9. The system according to claim 1, further comprising a fault isolator responsive to the break predictor for determining the paper machine variables affecting the predicted time-to-break of the paper web.

10. The system according to claim 9, wherein the fault isolator comprises an adaptive network-based fuzzy inference model having a set of rules linking paper machine variables to the predicted time-to-break of the paper web.

11. The system according to claim 9, wherein the fault isolator isolates the paper machine variables that are root causes for the predicted time-to-break of the paper web.

12. The system according to claim 1, further comprising an indicator mechanism for updating the status of the machine by indicating the predicted paper web time-to-break.

13. The system according to claim 1, further comprising a feedback mechanism for adjusting the performance of the break predictor.

14. The system according to claim 1, wherein the processor further processes the predicted time-to-break and prior predicted times-to-break into a final predicted time-to-break.

15. The system according to claim 1, wherein the plurality of measurements contained in the paper mill database are generated from various processes occurring within the paper mill.

16. The system according to claim 1, wherein the paper mill database comprises a raw materials database, a preprocess database, a paper machine database, an operation shift database and a maintenance schedule database.

17. A system for predicting a paper web break in a paper machine located about a paper mill, comprising:

a paper mill database containing a plurality of measurements from the paper mill, each of the plurality of measurements relating to a predetermined paper machine variable;

a processor for processing each of the plurality of measurements into modified break sensitivity data comprising time-based transformations of the plurality of data; and

a break predictor responsive to the processor for predicting a time-to-break of the paper web from the plurality of processed measurements, wherein the break predictor comprises a predictive model.

18. The system according to claim 17, wherein the predictive model comprises a neuro-fuzzy system.

19. The system according to claim 18, wherein the predictive model comprises an adaptive network-based fuzzy inference system.

20. The system according to claim 19, wherein the modified break sensitivity data comprise principal components of the plurality of measurements.

21. The system according to claim 20, further comprising a fault isolator that isolates the paper machine variables that are root causes for the predicted time-to-break of the paper web.

22. The system according to claim 20, further comprising an indicator mechanism for updating the status of the paper machine by indicating the predicted paper web time-to-break.

23. The system according to claim 20, further comprising a feedback mechanism for adjusting the performance of the break predictor.

24. The system according to claim 20, wherein the processor further processes the predicted time-to-break and prior predicted times-to-break into a final predicted time-to-break.

25. The system according to claim 17, wherein the plurality of measurements contained in the paper mill database are generated from various processes occurring within the paper mill.

26. The system according to claim 17, wherein the paper mill database comprises a raw materials database, a preprocess database, a paper machine database, an operation shift database and a maintenance schedule database.

27. A method for predicting a paper web break in a paper machine located about a paper mill, comprising:

obtaining a plurality of measurements from the paper mill, each of the plurality of measurements relating to a predetermined paper machine variable;

processing each of the plurality of measurements into modified break sensitivity data; and

predicting a time-to-break for the paper web within the paper machine from the plurality of processed measurements.

28. The method according to claim 27, wherein predicting the time-to-break for the paper web comprises applying a predictive model.

29. The method according to claim 27, wherein predicting the time-to-break for the paper web comprises applying a neuro-fuzzy system.

30. The method according to claim 27, wherein predicting the time-to-break for the paper web comprises applying an adaptive network-based fuzzy inference system.

31. The method according to claim 27, further comprising training the adaptive network-based fuzzy inference system with historical web break data.

32. The method according to claim 31, further comprising testing the trained adaptive network-based fuzzy inference system with the historical break data to test how well the system predicts the time-to-break.

33. The method according to claim 31, wherein the training comprises preprocessing the historical web break data.

34. The method according to claim 33, wherein the preprocessing comprises:

reducing the quantity of the historical web break data;

reducing the number of variables contained in the historical web break data;

transforming the values of the historical web break data;

enhancing features that affect web break sensitivity from the historical web break data; and

generating the adaptive network-based fuzzy inference system to predict the time-to-break.

35. The method according to claim 27, wherein the processing of the plurality of measurements into modified break sensitivity data further comprises time-based transformations of the plurality of measurements.

36. The method according to claim 27, wherein the processing of the plurality of measurements into modified break sensitivity data further comprises transforming the plurality of measurements into principal components for web breakage.

37. The method according to claim 27, further comprising processing the predicted time-to-break and prior predicted times-to-break into a final predicted time-to-break.

38. The method according to claim 27, further comprising adjusting the predicting of the time-to-break based on an analysis of the performance of the predicted time-to-break.

39. The method according to claim 27, further comprising updating the status of the paper machine by indicating the predicted time-to-break.

40. The method according to claim 27, further comprising isolating the paper machine variables affecting the predicted time-to-break.

41. The method according to claim 27, wherein the obtaining of the plurality of measurements comprises receiving measurements generated from various processes occurring within the paper mill.

42. A method for predicting a paper web break in a paper machine located about a paper mill, comprising:

obtaining a plurality of measurements from the paper mill, each of the plurality of measurements relating to a predetermined paper machine variable;

performing a time-based transformation of each of the plurality of measurements to produce modified break sensitivity data; and

predicting a time-to-break for the paper web within the paper machine from the plurality of processed measurements by applying a predictive model.

43. The method according to claim 42, wherein predicting the time-to-break for the paper web comprises applying a neuro-fuzzy system.

44. The method according to claim 42, wherein predicting the time-to-break for the paper web comprises applying an adaptive network-based fuzzy inference system.

45. The method according to claim 44, further comprising training the adaptive network-based fuzzy inference system with historical web break data.

46. The method according to claim 45, further comprising testing the trained adaptive network-based fuzzy inference system with the historical break data to test how well the system predicts the time-to-break.

47. The method according to claim 44, wherein performing the time-based transformation of the plurality of measurements into modified break sensitivity data further comprises transforming the plurality of measurements into principal components for web breakage.

48. The method according to claim 47, further comprising processing the predicted time-to-break and prior predicted times-to-break into a final predicted time-to-break.

49. The method according to claim 48, further comprising adjusting the predicting of the time-to-break based on an analysis of the performance of the predicted time-to-break.

50. The method according to claim 49, further comprising updating the status of the paper machine by indicating the predicted time-to-break.

51. The method according to claim 50, further comprising isolating the paper machine variables affecting the predicted time-to-break.

52. The method according to claim 42, wherein the obtaining of the plurality of measurements comprises receiving measurements generated from various processes occurring within the paper mill.