Automated self-learning diagnostic system

• US 20040117153A1
• Filed: 12/17/2002
• Published: 06/17/2004
• Est. Priority Date: 12/17/2002
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Zhao, Feng, Siegel, Robert P.
• Granted Patent: US 6,970,804 B2
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: H04L 41/0681 Configuration of triggering conditions
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence

Abstract

A self-learning diagnostic system provides diagnostics capabilities which may be applied to a population of networked machines or components and assemblies in a product. The self-learning diagnostic system uses both the components own historical data and the data for an entire population of networked machines of a given product in the field as the training set to adjust critical threshold parameters for detection and diagnosis. The system includes a nominal diagnostic threshold setting module which sets initial thresholds and an adjustment module, which adjusts thresholds continuously based on machine performance data. A service records evaluation module checks service records periodically for correlations and an adjustment module adjust service strategies based on correlation data.

Claims

1. A self-learning diagnostic system for a population of networked machines, comprising:

a nominal diagnostic threshold setting module

a threshold adjustment module;

a service records evaluation module; and

an adjustment module.

2. The self-learning diagnostic system according to claim 1, wherein said threshold adjustment module adjusts thresholds based on machine performance data.

3. The self-learning diagnostic system according to claim 1, wherein said service records evaluation module checks service records periodically for data correlations.

4. The self-learning diagnostic system according to claim 1, wherein said adjustment module adjusts service strategies based on correlation data.

5. The self-learning diagnostic system according to claim 1, wherein the self-learning diagnostic system provides machine service rules for service engineers or customers.

6. The self-learning diagnostic system according to claim 1, wherein said adjustment module continuously adjusts detection thresholds as components within the population of networked machines age.

7. The self-learning diagnostic system according to claim 1, wherein said adjustment module continuously adjusts detection thresholds as environmental conditions change.

18. The self-learning diagnostic system according to claim 1, wherein said adjustment module continuously adjusts detection thresholds as usage patterns vary.

8. A method for operating a self-learning diagnostic system for a population of networked machines, comprising:

receiving initial machine data;

determining a nominal set of diagnostic set points and thresholds;

performing self-learning diagnostics comprising;

collecting machine performance data from field machines;

updating set points and thresholds;

checking machine service records for undetected failures;

updating or adding machine service rules; and

adjusting diagnostic thresholds;

repeating performance of self-learning diagnostics continuously during operation of the population of networked machines; and

providing feedback to engineering divisions.

9. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, wherein updating set points and thresholds is based on statistical distribution of performance data.

10. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, wherein checking machine service records further comprises checking machine service records for correlation with performance data.

11. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, wherein updating or adding machine service rules is based on the correlation of machine service records with performance data.

12. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, wherein providing feedback further comprises providing feedback to product divisions.

13. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, further comprising performing said method in a centralized location.

14. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, further comprising performing said method with distributed machine clusters.

15. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, further comprising embedding said method in each machine of the population of networked machines.

16. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, further comprising performing said method off-line.

17. The method for operating a self-learning diagnostic system for a population of networked machines according to claim 8, further comprising performing said method on-line using real-time machine performance data over a network connection.

19. A method for operating a self-learning diagnostic system for a population of networked machines, comprising:

receiving initial machine data;

determining a nominal set of diagnostic set points and thresholds;

collecting machine performance data from field machines;

updating set points and thresholds; and

providing feedback to engineering divisions.

20. A method for operating a self-learning diagnostic system for a population of networked machines, comprising:

receiving machine service records;

checking machine service records for undetected failures;

updating or adding machine service rules;

adjusting diagnostic thresholds; and

providing feedback to engineering divisions.

21. A self-learning diagnostic system for a population of networked machines, comprising:

a nominal diagnostic threshold setting module; and

a threshold adjustment module.

22. A self-learning diagnostic system for a population of networked machines, comprising:

a service records evaluation module; and

an adjustment module.

23. An article of manufacture comprising a computer usable medium having computer readable program code embodied in said medium which, when said program code is executed by said computer causes said computer to perform method steps for operating a self-learning diagnostic system for a population of networked machines, said method comprising:

receiving initial machine data;

determining a nominal set of diagnostic set points and thresholds;

performing self-learning diagnostics comprising;

collecting machine performance data from field machines;

updating set points and thresholds;

checking machine service records for undetected failures;

updating or adding machine service rules; and

adjusting diagnostic thresholds;

repeating performance of self-learning diagnostics continuously during operation of the population of networked machines; and

providing feedback to engineering divisions.