SYSTEMS AND METHODS FOR PREDICTING MAINTENANCE OF INTELLIGENT ELECTRONIC DEVICES

• US 20090312999A1
• Filed: 06/17/2008
• Published: 12/17/2009
• Est. Priority Date: 06/17/2008
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (General Electric Company)
• Original Assignee: General Electric Company
• Inventors: Kasztenny, Bogdan Z., Mao, Zhihong, Mazereeuw, Jeffrey G., Sollecito, Lawrence A.
• Granted Patent: US 7,822,578 B2
• Time in Patent Office: Days
• US Class Current: 703/13
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 19/0425 Safety, monitoring
• CPC Class Codes: G05B 2219/24077 Module detects wear, changes of controlled device, statistical evaluation
• 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 11/008 Reliability or availability analysis
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y04S 20/20 End-user application control systems

Abstract

Predictive maintenance systems and methods are described. A method includes measuring environmental conditions using a plurality of sensors within the IED, processing the environmental measurements to determine long-term exposure factors representing historical operating conditions of the IED, applying a reliability model to the long-term exposure factors, determining a numerical measure of IED life based on the long-term exposure factors and the reliability model, comparing the numerical measure of IED life to preselected boundary values, and signaling if the numerical measure of IED life is outside of the preselected boundary values.

Claims

1. A method for predicting maintenance of an intelligent electronic device (IED), said method comprising:

measuring environmental conditions using at least one sensor associated the IED;

processing the measured environmental conditions to determine long-term exposure factors, said long-term exposure factors representing conditions of the IED operation;

applying the determined long-term exposure factors to a reliability model, said reliability model providing a relationship between known exposure factors and characteristics of said IED;

determining a numerical measure of IED life based on the determined long-term exposure factors and the reliability model;

comparing the numerical measure of IED life to preselected boundary values; and

providing a signal when the numerical measure of IED life is outside of the preselected boundary values.

2. A method in accordance with claim 1, wherein environmental conditions comprises at least one of:

a temperature, a peak electrical surge value, a grounding presence, a mechanical shock, a mechanical vibration, a chemical factor, a radiated electromagnetic interference, and a humidity.

3. A method in accordance with claim 1, wherein processing the measured environmental conditions to determine long-term exposure factors comprises:

determining at least one of;

raw sensor measurements, an integration of raw sensor measurements, an average value of raw sensor measurements, and a maximum value of raw sensor measurements.

4. A method in accordance with claim 1, further comprising developing the reliability model using reliability data of key IED components.

5. A method in accordance with claim 1, further comprising:

developing the reliability model using at least one of accelerated life testing results of the IED and field data obtained for a particular make of the IED.

6. A method in accordance with claim 1, wherein said reliability model is one of:

a deterministic reliability model, a stochastic reliability model, a reliability model based on fuzzy mathematics, and a reliability model based on an artificial neural network.

7. A method in accordance with claim 1, wherein determining a numerical measure of IED life comprises determining at least one of:

remaining life of the IED, used life of the IED, and a rate of wear of the IED.

8. A method in accordance with claim 7, wherein determining a numerical measure of IED life further comprises determining one of:

a ratio of actual wear to normal wear and a used life of the IED expressed in a number of time units.

9. A method in accordance with claim 7, wherein determining a rate of wear of the IED comprises:

determining a rate of wear based on operating conditions that are outside a specified range of acceptable operating conditions for the IED.

10. A method in accordance with claim 1, wherein providing a signal comprises generating a signal based on at least one of

a remaining life of the IED, a used life of the IED, and a rate of wear.

11. A method in accordance with claim 1, wherein providing a signal comprises

at least one of:

a visual indication, a physical output, a virtual point created in an operating code of the IED, and a virtual point created in a user programmable code of the IED.

12. A method in accordance with claim 1, further comprising:

storing the long-term exposure factors such that the long-term exposure factors are extractable upon a failure of the IED.

13. A method in accordance with claim 1, further comprising:

upon one of a partial repair of the IED and a refurbishment of the IED, augmenting the long-term exposure factors to reflect an actual wear of the IED.

14. A method in accordance with claim 1, further comprising integrating the reliability model into operating code of the IED.

15. A method in accordance with claim 1, further comprising storing the reliability model as a data entity.

16. A method in accordance with claim 15, further comprising upgrading the reliability model.

17. A method in accordance with claim 1, further comprising:

downloading the long-term exposure factors to a central application that determines a maintenance need for a plurality of IEDs based on the downloaded long-term exposure factors.

18. A method in accordance with claim 1, further comprising remotely updating the reliability model from a central application.

19. A method in accordance with claim 1, further comprising aggregating a plurality of reliability models of an associated plurality of IEDs.

20. A system for establishing and maintaining reliability models for a plurality of intelligent electronic devices (IEDs), said system comprising:

an acquisition unit configured to acquire long-term exposure factors from the plurality of IEDs;

an input unit configured to receive failure information from failed IEDs of the plurality of IEDs; and

a processor configured to be coupled to said acquisition unit and said input unit, said processor programmed to;

obtain a reliability of each IED of the plurality of IEDs; and

derive a reliability model, said reliability model providing a correlation between known exposure factors and the reliability of each IED of the plurality of IEDs.

21. A system in accordance with claim 20, wherein said acquisition unit is further configured to acquire long-term exposure factors from the plurality of IEDs using at least one of an electronic communication and a manual data entry.

22. A system in accordance with claim 20, wherein the long-term exposure factors are related to at least one of:

a temperature, a peak electrical surge value, a grounding presence, a mechanical shock, a mechanical vibration, a chemical factor, a radiated electromagnetic interference, and a humidity.

23. A system in accordance with claim 20, wherein the failure information includes at least one of:

a time of failure of the IED, a time of degraded performance of the IED, a time of failure of an IED subsystem, and a time of degraded performance of an IED subsystem.

24. A system in accordance with claim 20, wherein the reliability model predicts failures at least one of the plurality of IEDs and a single IED of the plurality of IEDs:

25. A system in accordance with claim 20, wherein the plurality of IEDs is user-controlled, and wherein said processor is further programmed to derive the reliability model based on the plurality of IEDs.

26. A system in accordance with claim 20, wherein said processor is further programmed to derive the reliability model based on data provided by a manufacturer of the plurality of IEDs.

27. A system in accordance with claim 20, wherein said processor is further programmed to derive the reliability model based on data provided by a manufacturer of the plurality of IEDs.

28. A system for monitoring operating conditions of an intelligent electronic device (IED) having a plurality of sensors therein for acquiring environmental data among a plurality of IEDs, said system comprising:

an acquisition unit configured to acquire long-term exposure factors from the plurality of sensors associated with selected ones of the IEDs;

an input unit configured to receive failure information from failed IEDs of the plurality of IEDs; and

a processor configured to be coupled to said acquisition unit and said input unit, said processor programmed to;

obtain a reliability of each of the plurality of IEDs;

derive a reliability model, said reliability model providing a correlation between known exposure factors and the reliability of each of the plurality of IEDs, said reliability model being further adapted based on said acquired long-term exposure factors and said received failure information;

determine a numerical measure of at least one IED based on the acquired sensor information associated with said IED;

compare the numerical measure of a remaining life of said at least one IED to preselected boundary values associated with said IED; and

generate a signal when the numerical measure of IED life is outside of the preselected boundary values.

29. A system in accordance with claim 28, wherein said processor is further configured to develop the reliability model using reliability data of key IED components.

30. A system in accordance with claim 29, wherein said processor is further programmed to determine a numerical measure of IED life by determining at least one of:

a remaining life of the IED, a used life of the IED, and a rate of wear based on operating conditions that are outside a specified range of acceptable operating conditions of the IED.

31. A system in accordance with claim 29, wherein said processor is further programmed to generate a signal based on at least one of a remaining life of the IED, a used life of the IED, and a rate of wear based on operating conditions that are outside a specified range of acceptable operating conditions of the IED.