METHOD AND SYSTEM FOR AUTOMATICALLY DIAGNOSING FAULTS IN RENDERING DEVICES

• US 20100325487A1
• Filed: 06/19/2009
• Published: 12/23/2010
• Est. Priority Date: 06/19/2009
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Wu, Wencheng, Monga, Vishal, Minhas, Rajinderjeet Singh
• Granted Patent: US 8,249,830 B2
• Time in Patent Office: Days
• US Class Current: 714/26
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06F 11/2257 using expert systems
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

A method and system for automatically determining an optimal re-training interval for a fault diagnoser based on online monitoring of the performance of a classifier. The classifier generates a soft measure of membership in association with a class based on a training data. The output of the classifier can be utilized to assign a label to new data and then the members associated with each class can be clustered into one or more core members and potential outliers. A statistical measure can be utilized to determine if the distribution of the outliers is sufficiently different than the core members after enough outliers have been accumulated. If the outliers are different with respect to the core members, then the diagnoser can be re-trained; otherwise, the output of the classifier can be fed to the fault diagnoser.

Claims

1. A method for automatically diagnosing new faults in a device, said method comprising:

generating a measure of membership with respect to each feature vector belonging to at least one known class among a plurality of known classes utilizing a classifier;

clustering a plurality of members associated with said at least one known class into two clusters including core class members and at least one outlier cluster in order to detect a presence of a new defect in a device; and

employing a statistical measure to determine if a probabilistic distribution of said plurality members of said outlier cluster associated with said class is sufficiently different from a corresponding distribution of said core class members to thereafter diagnose said new faults in said device.

2. The method of claim 1 further comprising initially mapping said known faults to user-defined classes to derive said plurality of known classes.

3. The method of claim 1 further comprising configuring said classifier based on training data, wherein said classifier assigns image quality based feature vectors to said at least one known class among said plurality of known classes.

4. A method for automatically diagnosing new faults in a device, said method comprising:

mapping known faults to user-defined classes to derive a plurality of known classes;

configuring a classifier based on training data, wherein said classifier assigns image quality based feature vectors to at least one known class among said plurality of known classes;

generating a measure of membership with respect to each feature vector belonging to said at least one known class utilizing said classifier;

clustering a plurality of members associated with said at least one known class into two clusters including core class members and at least one outlier cluster in order to detect a presence of a new defect in a device; and

employing a statistical measure to determine if a probabilistic distribution of said plurality members of said outlier cluster associated with said class is sufficiently different from a corresponding distribution of said core class members to thereafter diagnose said new faults in said device.

5. The method of claim 4 further comprising performing a corrective action with respect to said new defect in said device.

6. The method of claim 5 wherein said corrective action comprises re-training said classifier with updated expert identified classes based on said magnitude of said statistical measure.

7. The method of claim 4 further comprising assigning a label to said new defect based on a plurality of known faults utilizing said classifier.

8. The method of claim 4 further comprising associating a support vector machine classifier with said classifier.

9. The method of claim 4 further comprising:

extracting a raw image from a test pattern; and

transmitting said raw image to said classifier in the event a new defect is identified with respect to an image quality associated with said device.

10. The method of claim 4 further comprising configuring said classifier to generate a flag if said classifier detects statistical evidence for the existence of said new defect.

11. The method of claim 4 further comprising training said classifier with respect to a plurality of images associated with at least one symptom.

12. A system for automatically diagnosing new faults in a device, said system comprising:

a processor;

a data bus coupled to said processor; and

a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for;

mapping known faults to user-defined classes to derive a plurality of known classes;

configuring a classifier based on training data, wherein said classifier assigns image quality based feature vectors to at least one known class among said plurality of known classes;

generating a measure of membership with respect to each feature vector belonging to said at least one known class utilizing said classifier;

clustering a plurality of members associated with said at least one known class into two clusters including core class members and at least one outlier cluster in order to detect a presence of a new defect in a device; and

employing a statistical measure to determine if a probabilistic distribution of said plurality members of said outlier cluster associated with said class is sufficiently different from a corresponding distribution of said core class members to thereafter diagnose said new faults in said device.

13. The system of claim 12 wherein said instructions are further configured for performing a corrective action with respect to said new defect in said device.

14. The system of claim 13 wherein said corrective action comprises re-training said classifier with updated expert identified classes based on said magnitude of said statistical measure.

15. The system of claim 12 wherein said instructions are further configured for assigning a label to said new defect based on a plurality of known faults utilizing said classifier.

16. The system of claim 12 wherein said instructions are further configured for associating a support vector machine classifier with said classifier.

17. The system of claim 12 wherein said instructions are further configured for:

extracting a raw image from a test pattern; and

transmitting said raw image to said classifier in the event a new defect is identified with respect to an image quality associated with said device.

18. The system of claim 12 wherein said instructions are further configured for configuring said classifier to generate a flag if said classifier detects statistical evidence for the existence of said new defect.

19. The system of claim 12 wherein said instructions are further configured for training said classifier with respect to a plurality of images associated with at least one symptom.

20. The system of claim 12 wherein said instructions are further configured for:

assigning a label to said new defect based on a plurality of known faults utilizing said classifier; and

associating a support vector machine classifier with said classifier.