Apparatus and method for the analysis of a process having parameter-based faults

• US 7,536,371 B2
• Filed: 12/05/2006
• Issued: 05/19/2009
• Est. Priority Date: 12/05/2005
• Status: Expired due to Fees

• Current Assignee: ADA ANALYTICS ISRAEL LTD (Advanced Emissions Solutions, Inc.)
• Original Assignee: Insyst, Ltd. (Advanced Emissions Solutions, Inc.)
• Inventors: Kokolov, Yuri, Hartman, Jehuda, Brill, Eyal
• Primary Examiner(s): Holmes; Michael B
• Application Number:
  US11/633,455
• Publication Number:
  US 20070156620A1
• Time in Patent Office: 896 Days
• Field of Search: None
• US Class Current: 706/47
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0248 Causal models, e.g. fault tree; digraphs; qualitative physics
• CPC Class Codes: G05B 23/0272 Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
• CPC Class Codes: G06F 18/2321 using statistics or function optimisation, e.g. modelling of probability density functions
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputer configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputer and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to asses a process status.

Claims

1. An apparatus for the analysis of a process having parameter-based faults, comprising:

a parameter value inputer configured for inputting values of at least one process parameter;

a fault detector, configured for detecting the occurrence of a fault;

a learning file creator associated with said parameter value inputer and said fault detector, configured for separating said input values into a first learning file and a second learning file, said first learning file comprising input values from a collection period preceding each of said detected faults, and said second learning file comprising input values input outside said collection periods; and

a learning file analyzer associated with said learning file creator, configured for performing a separate statistical analysis of said first and second learning files, thereby to assess a process status.

2. The apparatus of claim 1, wherein said learning file analyzer is configured to separately convert said first and second learning files into a respective first and second probability density of occurrence, for at least pairs of parameters.

3. The apparatus of claim 2, wherein said learning file analyzer is further configured to superimpose said first and second probability densities of occurrence onto a single graph, and to determine a fault risk level over said graph with respect to said superimposed probability densities.

4. The apparatus of claim 3, wherein said learning file analyzer is further configured to interpolate fault risk levels determined over a plurality of graphs to obtain an overall risk.

5. The apparatus of claim 1, wherein said at least one process parameter comprises a controllable process parameter.

6. The apparatus of claim 1, wherein said fault detector is configured for automatic fault detection.

7. The apparatus of claim 1, wherein said fault detector is configured to receive user inputs indicating the occurrence of a fault.

8. The apparatus of claim 1, wherein said learning file creator is configured to estimate a duration of a pre-fault period in which a causal relationship exists between process parameter values and said fault, and to set said collection period to less than said estimated duration.

9. The apparatus of claim 1, wherein a duration of said collection period is specifiable by a user.

10. The apparatus of claim 1, further comprising a process modeler operable to provide a model of said process.

11. The apparatus of claim 10, wherein said process modeler is configured to model said process as a knowledge tree mapping of relationships between said process parameters and process outputs.

12. The apparatus of claim 10, further comprising a clusterer associated with said process modeler, for clustering interrelated process parameters into at least one cluster.

13. The apparatus of claim 12, wherein said learning file analyzer is configured to perform joint statistical analyses of clustered parameters.

14. The apparatus of claim 13, wherein said joint statistical analysis comprises providing a multi-dimensional probability density function of said clustered parameters.

15. The apparatus of claim 12, wherein each of said clusters is associated with a respective collection duration.

16. The apparatus of claim 1, further comprising a control unit configured to determine a preferred value of a controllable parameter so as to reduce a probability of a fault.

17. The apparatus of claim 16, wherein said control unit is further configured to automatically adjust a value of said controllable parameter to said preferred value.

18. The apparatus of claim 1, further comprising a discretizer configured for dividing a range of input parameter values into discrete sub-ranges.

19. The apparatus of claim 18, wherein said discretizer is operable to select split values for said sub-ranges so as to maximize an average time between faults while retaining a statistically-significant sample within a range.

20. The apparatus of claim 1, further comprising a risk evaluator configured for evaluating a fault risk level for a process having a specified set of process parameter values.

21. The apparatus of claim 20, wherein said fault risk level comprises at least one of a group consisting of:

an estimated time until fault, and an estimated probability of a fault within a specified time interval.

22. The apparatus of claim 20, wherein said risk evaluator comprises a parameter array generator configured for maintaining a risk level array, each dimension of said array comprising a respective process parameter subdivided into ranges, and for generating for each element of said array a respective fault risk level estimation in accordance with said statistical analyses.

23. The apparatus of claim 22, wherein said fault risk level is determined over a set of historically-collected data.

24. The apparatus of claim 22, wherein said fault risk level comprises a binary indicator indicating high-risk and low-risk process conditions.

25. The apparatus of claim 22, wherein said risk evaluator is operable to utilize said risk level array as a lookup table, and to determine a risk level for a specified set of parameter values from a corresponding element of said risk level array.

26. The apparatus of claim 1, further comprising a process monitor for monitoring parameter values of an ongoing process so as to determine a current status of said ongoing process.

27. The apparatus of claim 26, wherein said process monitor is configured to provide alerts to a process operator in accordance with said current status.

28. The apparatus of claim 1, wherein said process comprises papermaking and said fault comprises a paper break.

29. A method for the analysis of a process having parameter-based faults, comprising:

inputting the values of at least one process parameter over time;

detecting occurrences of process faults;

separating said input values into a first learning file and a second learning file, said first learning file comprising input values from a collection period preceding each of said detected faults, and said second learning file comprising input values input outside said collection periods; and

performing a separate statistical analysis of said first and second learning files, thereby to asses a process status for a specified set of parameter values.

30. The method of claim 29, further comprising separately converting said first and second learning files into a respective first and second probability density of occurrence, for at least pairs of parameters.

31. The method of claim 30, further comprising superimposing said first and second probability densities of occurrence onto a single graph, and determining a fault risk level over said graph with respect to said superimposed probability densities.

32. The method of claim 31, further comprising interpolating fault risk levels determined over a plurality of graphs to obtain an overall risk.

33. The method of claim 29, wherein said specified set comprise current process parameter values.

34. The method of claim 29, wherein said process status comprises at least one of a group consisting of:

an estimated time until fault, and an estimated probability of a fault within a specified time interval.

35. The method of claim 29, further comprising receiving an external indicator of the occurrence of a fault.

36. The method of claim 29, further comprising estimating a duration of a pre-fault period in which a causal relationship exists between process parameter values and said fault, and setting said collection period to less than said estimated duration.

37. The method of claim 29, wherein receiving a user input indicating a collection period duration.

38. The method of claim 29, further comprising providing a model of said process.

39. The method of claim 29, further comprising determining a preferred value for a controllable parameter so as to reduce a probability of a fault.

40. The method of claim 39, further comprising adjusting said controllable parameter to said preferred value.

41. The method of claim 29, further comprising forming clusters of interrelated process parameters.

42. The method of claim 41, wherein said statistical analysis comprises providing a multi-dimensional probability density function of clustered parameters.

43. The method of claim 41, wherein each of said clusters is associated with a respective collection period duration.

44. The method of claim 29 further comprising dividing a range of input parameter values into discrete sub-ranges.

45. The method of claim 44, wherein said ranges are selected so as to maximize an average time between faults while retaining a statistically significant sample within a range.

46. Method of claim 29, further comprising evaluating a fault risk level for a process having a specified set of process parameter values.

47. The method of claim 29, further comprising maintaining a risk level array, each dimension of said array comprising a respective process parameter subdivided into ranges, and generating for each element of said array a respective fault risk level in accordance with said statistical analyses.

48. The method of claim 47, wherein said risk level estimation comprises a binary indicator indicating high-risk and low-risk process conditions.

49. The method of claim 47, further comprising a risk determiner configured for looking-up a risk level estimation of a specified set of parameter values in said array.

50. The method of claim 29, further comprising providing alerts to a process operator in accordance with a current process status.