METHOD AND SYSTEM FOR DETERMINING THE RELIABILITY OF A DC MOTOR SYSTEM

• US 20090096406A1
• Filed: 10/15/2007
• Published: 04/16/2009
• Est. Priority Date: 10/15/2007
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Chmiel, Gary J., Flickinger, Greg L., Sutherland, Hunt A.
• Granted Patent: US 7,873,581 B2
• Time in Patent Office: Days
• US Class Current: 318/565
• 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

Abstract

A method and system for determining the reliability and a remaining time before failure for a DC motor system is provided. The method and system may determine the reliability and a remaining time before failure with a statistical confidence. The method and system may includes 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 DC motor system.

Claims

1. A method of determining reliability of at least one DC motor system on a site, the method comprising:

providing at least one DC condition forecaster, wherein the at least one DC condition forecaster is located on the site having the at least one DC motor system;

acquiring historical motor data corresponding to the at least one DC motor system from at least one historical database;

wherein the historical motor data comprises data representing an armature current;

a field current;

a field temperature;

a bearing temperature;

vibration;

speed;

commutator arcing; and

combinations thereof;

receiving operational indicator data from at least one sensor of the DC motor system;

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

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

performing at least one integrated causal network and reliability analysis of the at least one DC motor system, wherein results from the at least one analysis are integrated with results from the step of assessing the at least one DC motor system component condition based on the at least one causal network to compute a quantitative value for an estimated time remaining before the failure; and

generating at least one operability curve of the DC motor system based in part on the results of the step of performing the at least one integrated causal network and reliability analysis of the at least one DC motor system.

2. The method of claim 1, wherein the step of developing the causal network comprises utilizing fuzzy logic.

3. The method of claim 1, wherein the step of performing at least one integrated causal network and reliability analysis of the at least one DC motor system comprises modeling a failure rate of a system component using a Weibull probability distribution function.

4. The method of claim 1, wherein the step of receiving operational indicator data from at least one sensor of the at least one DC motor system further comprises transferring the operational indictor data to the historical database.

5. The method of claim 1 wherein the step of receiving operational indicator data from at least one sensor of the at least one DC motor system;

the at least one sensor comprises at least one of a commutator sensor;

a spark detection sensor;

or an arcing detection sensor; and

combinations thereof.

6. The method of claim 1, wherein the operational indictor data comprises data representing:

an armature current;

a field current;

a field temperature;

a bearing temperature;

vibration;

commutator arcing;

speed; and

combinations thereof.

7. The method of claim 1 further comprising the step of providing a graphical user interface that displays an estimated remaining time before failure of at least one DC motor system component.

8. A method of determining reliability of at least one DC motor system on a site, the method comprising:

providing at least one DC condition forecaster, wherein the at least one DC condition forecaster is located on the site having the at least one DC motor system;

acquiring historical motor data corresponding to the at least one DC motor system from at least one historical database;

wherein the historical motor data comprises data representing an armature current;

a field current;

a field temperature;

a bearing temperature;

vibration;

speed;

commutator arcing; and

combinations thereof;

receiving operational indicator data from at least one sensor of the DC motor system;

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

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

wherein the causal network utilizes fuzzy logic;

performing at least one integrated causal network and reliability analysis of the at least one DC motor system, wherein results from the at least one analysis are integrated with results of assessing the at least one DC motor system component condition based on the at least one causal network to compute a quantitative value for an estimated time remaining before the failure; and

modeling a failure rate of a system component using a Weibull probability distribution function; and

generating at least one operability curve of the DC motor system based in part on the results of the step of performing the at least one integrated causal network and reliability analysis of the at least one DC motor system.

9. The method of claim 8, wherein the step of receiving operational indicator data from at least one sensor of the at least one DC motor system further comprises transferring the operational indictor data to the historical database.

10. The method of claim 8 wherein the step of receiving operational indicator data from at least one sensor of the at least one DC motor system;

the at least one sensor comprises at least one of a commutator sensor;

a spark detection sensor;

or an arcing detection sensor; and

combinations thereof.

11. The method of claim 8, wherein the operational indictor data comprises data representing:

an armature current;

a field current;

a field temperature;

a bearing temperature;

vibration;

commutator arcing;

speed; and

combinations thereof.

12. The method of claim 8 further comprising the step of providing a graphical user interface that displays an estimated remaining time before failure of at least one DC motor system component.

13. A system for determining reliability of at least one DC motor system on a site, the system comprising:

at least one DC condition forecaster, wherein the at least one DC condition forecaster is located on the site having the at least one DC motor system;

means for acquiring historical motor data corresponding to the at least one DC motor system from at least one historical database;

at least one sensor of the DC motor system;

wherein the at least one sensor provides operational indicator data;

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

means for developing at least one causal network for modeling reliability of a plurality of DC motor systems, including the at least one DC motor system;

means for assessing a combination of at least one DC motor system component based on the causal network; and

wherein the at least one DC condition forecaster;

performs at least one integrated causal network and reliability analysis of the at least one DC motor system, wherein results from the at least one analysis are integrated with results from the step of assessing the at least one DC motor system component condition based on the at least one causal network to compute a quantitative value for an estimated time remaining before the failure; and

generates at least one operability curve of the at least one DC motor system based in part on the results of the step of performing the at least one integrated causal network and reliability analysis of the at least one DC motor system.

14. The system of claim 13, wherein the at least one DC motor condition forecaster comprises fuzzy logic.

15. The system of claim 13, wherein the DC motor condition forecaster comprises a Weibull probability distribution function.

16. The system of claim 13 further comprising means for transferring the operational indictor data to the historical database.

17. The system of claim 13 wherein the at least one sensor of the at least one DC motor system further comprises at least one of a commutator sensor;

a spark detection sensor;

or an arcing detection sensor; and

combinations thereof.

18. The system of claim 13, wherein the operational indictor data comprises data representing:

an armature current;

a field current;

a field temperature;

a bearing temperature;

vibration;

commutator arcing;

speed; and

combinations thereof.

19. The system of claim 13 further comprising a graphical user interface that displays an estimated remaining time before failure of at least one DC motor system component.

20. The system of claim 13, wherein the historical motor data comprises data representing an armature current;

a field current;

a field temperature;

a bearing temperature;

vibration;

commutator arcing;

speed; and

combinations thereof.