METHOD AND SYSTEM FOR REMOTELY PREDICTING THE REMAINING LIFE OF AN AC MOTOR SYSTEM

• US 20090096405A1
• Filed: 10/15/2007
• Published: 04/16/2009
• Est. Priority Date: 10/15/2007
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Johnson, David N., Chmiel, Gary J., Flickinger, Greg L.
• Application Number:
  US11/872,211
• Publication Number:
  US 20090096405A1
• Time in Patent Office: Days
• US Class Current: 318/565
• CPC Class Codes: G01R 31/343 in operation

Abstract

A method and system for remotely predicting the reliability and the remaining time before failure for an AC motor system is provided. The method and system may remotely determine the reliability and a remaining time before failure with a statistical confidence utilizing an AC motor condition forecaster. The method and system may include acquiring historical motor data, obtaining operational data, performing failure analysis, developing a causal network, and performing an integrated causal network and reliability analysis of the AC motor system. The method and system may provide at least one notification of an issue with the AC motor system or at least one component of the AC motor system.

Claims

1. A method of remotely determining both reliability and a remaining time before a failure for at least one AC motor system located on a site, the method comprising:

providing at least one remote monitoring and diagnostics (RM&

D) system, wherein the at least one RM&

D system is at a location different from the site having the at least one AC motor system;

integrating the at least one RM&

D system with at least one condition forecaster, wherein the at least one condition forecaster receives a plurality of operating data from the at least one AC motor system;

receiving the plurality of operating data from the condition forecaster;

transmitting a plurality of historical operating data corresponding to the at least one AC motor system from at least one historical database to the at least one RM&

D system;

utilizing the plurality of operating data to determine whether reliability and the remaining time before the failure of the at least one AC motor system are within an alarm range; and

if reliability and the remaining time before the failure of the at least one AC motor system are within the alarm range;

then notifying at least one support network.

2. The method of claim 1, wherein the step of integrating the at least one RM&

D system with the at least one condition forecaster further comprises;

the at least one condition forecaster performing at least one failure analysis based on a composite of reliability probability distributions corresponding to predetermined sub-populations of historical failure causes relating to the at least one AC motor system;

the at least one condition forecaster developing at least one causal network for modeling reliability of a plurality of AC motor systems, including the at least one AC motor system and assessing at least one AC motor system component condition based on the at least one causal network; and

the at least one condition forecaster performing at least one integrated causal network and reliability analysis of the at least one AC motor system, wherein results from the at least one analysis is integrated with results from the step of assessing the at least one AC motor system component condition based on the at least one causal network to compute a quantitative value for a time remaining before the failure.

3. The method of claim 1, wherein the step of receiving the plurality of operating data from the condition forecaster further comprises receiving at least one leakage current data from at least one tan delta sensor.

4. The method of claim 2, wherein the step of developing the at least one causal network further comprises utilizing Fuzzy Logic.

5. The method of claim 2, wherein the step of performing the at least one failure analysis comprises modeling a failure rate of at least one component of the AC motor system using a Weibull probability distribution function.

6. The method of claim 2, wherein the step of receiving the at least one leakage current data further comprises receiving at least one leakage current phase data.

7. The method of claim 2 further comprising determining an insulation breakdown responsive to the leakage current data.

8. The method of claim 1, further comprising integrating the at least one RM&

D system with at least one remote AC motor system, wherein the at least one remote AC motor system is at a location different from the site having the at least one AC motor system.

9. A method of remotely determining both reliability and a remaining time before a failure for at least one AC motor system located on a site, the method comprising

providing at least one remote monitoring and diagnostics (RM&

D) system, wherein the at least one RM&

D system is at a location different from the site having the at least one AC motor system;

integrating the at least one RM&

D system with at least one condition forecaster, wherein the at least one condition forecaster receives a plurality of operating data from the at least one AC motor system;

wherein the at least one condition forecaster comprises the steps of;

performing at least one failure analysis based on a composite of reliability probability distributions corresponding to predetermined sub-populations of historical failure causes relating to the at least one AC motor system;

developing at least one causal network for modeling reliability of a plurality of AC motor systems, including the at least one AC motor system and assessing at least one AC motor system component condition based on the causal network; and

performing at least one integrated causal network and reliability analysis of the at least one AC motor system utilizing a Weibull probability distribution function;

wherein results from the at least one analysis is integrated with results from the step of assessing the at least one AC motor system component condition based on the at least one causal network to compute a quantitative value for a time remaining before the failure;

transmitting the plurality of operating data from the condition forecaster to the at least one RM&

D system, wherein the plurality of operating data comprises data from at least one tan delta sensor;

receiving a plurality of historical operating data corresponding to the at least one AC motor system from at least one historical database;

utilizing the plurality of operating data to determine whether reliability and a remaining time before a failure of the at least one AC motor system are within an alarm range; and

if reliability and the remaining time before the failure of the at least one AC motor system are within the alarm range;

then notifying at least one support network.

10. The method of claim 9, wherein the step of receiving the plurality of operating data of the at least one AC motor system further comprises receiving at least one leakage current data;

wherein the at least one leakage current data comprises data from at least one tan delta sensor; and

wherein the at least one leakage current data comprises data from at least one leakage current phase data.

11. The method of claim 10, wherein the step of performing the at least one failure analysis further comprises determining an insulation breakdown of at least one AC motor system component responsive to leakage current data measured at the at least one AC motor system.

12. The method of claim 9, further comprising integrating the at least one RM&

D system with at least one remote AC motor system, wherein the at least one remote AC motor system is at a location different from the site having the at least one AC motor system.

13. A system for remotely determining both reliability and a remaining time before a failure for at least one AC motor system located on a site, the system comprising:

at least one remote monitoring and diagnostics (RM&

D) system, wherein the at least one RM&

D system is located at a site different from the site having the at least one AC motor system, wherein the at least one RM&

D system monitors a plurality of operating data of the at least one AC motor system;

at least one condition forecaster, wherein the at least one condition forecaster;

performs at least one failure analysis based on a composite of reliability probability distributions corresponding to predetermined sub-populations of historical failure causes relating to the at least one AC motor system;

develops at least one causal network for modeling reliability of a plurality of AC motor systems, including the at least one AC motor system and assessing at least one AC motor system component condition based on the causal network; and

performs at least one integrated causal network and reliability analysis of the at least one AC motor system;

wherein results from the at least one analysis is integrated with results from the step of assessing the at least one AC motor system component condition based on the at least one causal network to compute a quantitative value for a time remaining before the failure;

wherein the plurality of operating data comprises data from at least one tan delta sensor;

means for integrating the at least one RM&

D system with the at least one condition forecaster; and

means for transmitting the plurality of operating data from the condition forecaster to the at least one RM&

D system.

14. The system of claim 13, further comprising means for integrating the at least one RM&

D system with at least one remote AC motor system;

wherein the at least one remote AC motor system is at a location different from the site having the at least one AC motor system.

15. The system of claim 13, wherein the at least one condition forecaster receives at least one leakage current data,

wherein the at least one leakage current data comprises data from the at least one tan delta sensor;

and

wherein the at least one leakage current data further comprises at least one leakage current phase data.

16. The system of claim 13, wherein the at least one condition forecaster further comprises at least one structure, wherein the at least one structure predicts an insulation breakdown responsive to a leakage current data;

and an insulation breakdown of at least one AC motor system component responsive to the leakage current data measured at the at least one AC motor system.

17. The system of claim 13, wherein the at least one causal network comprises means for utilizing Fuzzy Logic.

18. The system of claim 13, wherein the at least one condition forecaster comprises means for utilizing a Weibull probability distribution function.

19. The system of claim 13 further comprising:

means for receiving a plurality of historical operating data corresponding to the at least one AC motor system from at least one historical database;

means for utilizing the plurality of operating data to determine whether reliability and a remaining time before a failure of the at least one AC motor system are within an alarm range; and

means for determining if reliability and the remaining time before failure of the at least one AC motor system are within the alarm range; and

for notifying at least one support network.