USING FAULT HISTORY TO PREDICT REPLACEMENT PARTS

• US 20080059120A1
• Filed: 08/30/2006
• Published: 03/06/2008
• Est. Priority Date: 08/30/2006
• Status: Abandoned Application

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Minhas, Rajinderjeet Singh, Xiao, Fei
• Application Number:
  US11/468,443
• Publication Number:
  US 20080059120A1
• Time in Patent Office: Days
• US Class Current: 702/184
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/0748 in a remote unit communicating with a single-box computer node experiencing an error/fault remote testing G06F11/2294
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36

Abstract

Embodiments herein include a method, computer program, etc., that establishes a first database of part replacement and fault occurrence history based on maintenance records and device data for a plurality of very similar or identical devices (fleet of identical (e.g., same model number) devices, such as electrostatic printing devices). The method creates a model based on information within the first database that links sequences of faults to specific replacement parts for the plurality of identical devices. In addition, the method can maintain a second database of repair history for a specific device within the fleet. This allows the method to predict which part or parts (repair parts) are needed for the specific device by applying the model to a sequence of fault codes for the specific device. In addition to the fault codes, the model can also consider the history of the specific device within the second database.

Claims

1. A method comprising:

establishing a database of part replacement and fault occurrence history comprising device symptoms based on human generated maintenance records and device maintained data for a plurality of devices;

creating a model based on information within said database that links sequences of faults to specific replacement parts for said plurality of devices;

remotely predicting part need probabilities for a specific device by applying said model to faults for said specific device and to a history of said specific device, wherein said remotely predicting comprises matching a sequence of fault codes of said history of said specific device with patterns within said information within said database; and

outputting to a user a list of potential replacement parts and corresponding part need probabilities for each potential replacement part on said list,

wherein each of said part need probabilities comprises a percentage probability that a corresponding potential replacement part will be needed for said specific device.

2. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said creating comprises data mining of said information within said database.

3. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said creating comprises at least one of attribute selection, decision tree, Bayesian network, association mining and rule extraction.

4. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said creating comprises an iterative process.

5. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said creating comprises identifying patterns within said information within said database.

7. The method according to claim 1, all the limitations of which are incorporated herein by reference, wherein said remotely predicting comprises performing said predicting at a location physically separate from said specific device, such that a repair technician does not have physical access to said specific device.

6. (canceled)

8. A method comprising:

establishing a first database of part replacement and fault occurrence history comprising device symptoms based on human generated maintenance records and device maintained data for a plurality of identical devices;

creating a model based on information within said first database that links sequences of faults to specific replacement parts for said plurality of identical devices;

maintaining a second database of repair history for a specific device within said plurality of identical devices;

remotely predicting part need probabilities for said specific device by applying said model to faults for said specific device and to a history of said specific device within said second database, wherein said remotely predicting comprises matching a sequence of fault codes of said history of said specific device with patterns within said information within said first database; and

outputting to a user a list of potential replacement parts and corresponding part need probabilities for each potential replacement part on said list,

wherein each of said part need probabilities comprises a percentage probability that a corresponding potential replacement part will be needed for said specific device.

9. The method according to claim 8, all the limitations of which are incorporated herein by reference, wherein said creating comprises data mining of said information within said first database.

10. The method according to claim 8, all the limitations of which are incorporated herein by reference, wherein said creating comprises at least one of attribute selection, decision tree, Bayesian network, association mining and rule extraction.

11. The method according to claim 8, all the limitations of which are incorporated herein by reference, wherein said creating comprises an iterative process.

12. The method according to claim 8, all the limitations of which are incorporated herein by reference, wherein said creating comprises identifying patterns within said information within said first database.

14. The method according to claim 8, all the limitations of which are incorporated herein by reference, wherein said remotely predicting comprises performing said predicting at a location physically separate from said specific device, such that a repair technician does not have physical access to said specific device.

13. (canceled)

15. A method comprising:

establishing a first database of part replacement and fault occurrence history comprising device symptoms based on human generated maintenance records and device maintained data for a fleet of identical electrostatic printing devices;

creating a model based on information within said first database that links sequences of faults to specific replacement parts for said fleet;

maintaining a second database of repair history for a specific device within said fleet;

remotely predicting part need probabilities for said specific device by applying said model to faults for said specific device and to a history of said specific device within said second database, wherein said remotely predicting comprises matching a sequence of fault codes of said history of said specific device with patterns within said information within said first database; and

outputting to a user a list of potential replacement parts and corresponding part need probabilities for each potential replacement part on said list,

wherein each of said part need probabilities comprises a percentage probability that a corresponding potential replacement part will be needed for said specific device.

16. The method according to claim 15, all the limitations of which are incorporated herein by reference, wherein said creating comprises data mining of said information within said first database.

17. The method according to claim 15, all the limitations of which are incorporated herein by reference, wherein said creating comprises at least one of attribute selection, decision tree, Bayesian network, association mining and rule extraction.

18. The method according to claim 15, all the limitations of which are incorporated herein by reference, wherein said creating comprises an iterative process.

19. The method according to claim 15, all the limitations of which are incorporated herein by reference, wherein said remotely predicting comprises performing said predicting at a location physically separate from said specific device, such that a repair technician does not have physical access to said specific device.

20. A computer program product comprising:

a computer-usable data carrier storing instructions that, when executed by a computer, cause a computer to perform a method comprising;

establishing a database of part replacement and fault occurrence history comprising device symptoms based on human generated maintenance records and device maintained data for a plurality of devices;

creating a model based on information within said database that links sequences of faults to specific replacement parts for said plurality of devices;

remotely predicting part need probabilities for a specific device by applying said model to a fault for said specific device and to a history of said specific device, wherein said remotely predicting comprises matching a sequence of fault codes of said history of said specific device with patterns within said information within said database; and

outputting to a user a list of potential replacement parts and corresponding part need probabilities for each potential replacement part on said list,

wherein each of said part need probabilities comprises a percentage probability that a corresponding potential replacement part will be needed for said specific device.