Systems and methods for predicting failure of electronic systems and assessing level of degradation and remaining useful life

• US 8,103,463 B2
• Filed: 09/21/2007
• Issued: 01/24/2012
• Est. Priority Date: 09/21/2006
• Status: Active Grant

• Current Assignee: Sikorsky Aircraft Corporation (Martin Marietta Corporation)
• Original Assignee: Impact Technologies LLC (Martin Marietta Corporation)
• Inventors: Kalgren, Patrick W., Ginart, Antonio E., Nanduri, Sashank, Boodhansingh, Anthony J., Byington, Carl S., Orsagh, Rolf F., Sipos, Brian J., Brown, Douglas W., Minnella, Christopher M., Baybutt, Mark
• Primary Examiner(s): Charioui, Mohamed
• Assistant Examiner(s): Desta, Elias
• Application Number:
  US11/859,256
• Publication Number:
  US 20080141072A1
• Time in Patent Office: 1,586 Days
• Field of Search: 702/59, 702/64, 702/81, 702/109, 702/117, 702/179, 702/182, 702/189, 714/41, 714/42, 714/44, 379/2, 204/400
• US Class Current: 702/57
• CPC Class Codes: G01D 18/00 Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
• CPC Class Codes: G01D 3/08 with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
• CPC Class Codes: G01R 31/2834 Automated test systems [ATE]; using microprocessors or computers G01R31/317 takes precedence; ATE for detection of defective computer hardware G06F11/2736

Abstract

Disclosed are systems and methods for prognostic health management (PHM) of electronic systems. Such systems and methods present challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. The systems and methods are directed to the health monitoring and failure prediction of electronic systems, including the diagnostic methods employed to assess current health state and prognostic methods for the prediction of electronic system failures and remaining useful life. The disclosed methodologies include three techniques: (1) use of existing electronic systems data (circuit as a sensor); (2) use of available external measurements as condition indicators and degradation assessor; and (3) performance assessment metrics derived from available external measurements.

Claims

1. A method for monitoring the health-state for electronic equipment, comprising:

measuring current and voltage at an input and an output of the electronic equipment and acquiring data therefrom;

storing the measured data in a memory;

using a processor having access to the measured data, calculating performance metrics for the equipment;

separating the measured data into a plurality of data classes;

generating performance models for at least one data class;

extracting diagnostic features from measured data values by comparing calculated performance metrics with the performance models; and

identifying the source and severity of a fault based upon the diagnostic features.

2. The method according to claim 1, wherein extracting diagnostic features includes statistical analysis.

3. The method according to claim 1, wherein extracting diagnostic features includes trend analysis.

4. The method according to claim 1, wherein extracting diagnostic features includes threshold analysis.

5. The method according to claim 1, wherein extracting diagnostic features includes pattern analysis.

6. The method according to claim 1, wherein extracting diagnostic features includes quantitative state estimation.

8. The method according to claim 1, further including displaying the health assessment to an operator.

10. The method according to claim 1, wherein calculating performance metrics includes performing calculations using explicit analytical expressions in terms of measured data values.

11. The method according to claim 1, wherein the performance metrics include loss resistance, power loss, and converter efficiency.

12. The method according to claim 1, wherein generating performance models includes using an analytical best-fit expression that relates the performance metrics with the monitoring values for each data class.

13. The method according to claim 1, wherein generating performance models includes using a neural network that relates the performance metrics with the monitoring values for each data class.

18. The method according to claim 1, wherein the electronic equipment is an avionic system.

19. The method according to claim 1, wherein the electronic equipment is a navigational system.

20. The method according to claim 1, wherein the electronic equipment is a radio-frequency system.

23. The method according to claim 1, wherein measuring current and voltage at an input and an output of the electronic equipment and acquiring data therefrom is accomplished in real-time without interrupting operation of the electronic equipment.

24. The method according to claim 1, wherein the model is a model of the ringing oscillation observed during a transistor transition.

25. The method according to claim 1, wherein the model further includes an aging component.

26. The method according to claim 1, wherein the diagnostic features are extracted by comparing a measured performance history with a diagnostic model for each class.

27. The method according to claim 1, wherein diagnostic features are generated from at least one of the plurality of data classes and the diagnostic features are employed to detect health variations within for at least one electronic circuit component.

28. The method of claim 1, further including using a plurality of environmental sensors to measure various environmental parameters in which the electronic equipment operates, each of the environmental sensors producing a signal representing a respective environmental parameter.

29. The method according to claim 28, wherein the signals collected by the environmental sensors are used to generate hybrid models to account for feature variation caused by changes in environmental conditions.

7. A method for monitoring the health-state for electronic equipment, comprising:

measuring current and voltage at an input and an output of the electronic equipment and acquiring data therefrom;

storing the measured data in a memory;

using a processor having access to the measured data, calculating performance metrics for the equipment;

separating the measured data into a plurality of data classes;

generating performance models for at least one data class;

extracting diagnostic features from measured data values by comparing calculated performance metrics with the performance models, wherein extracting diagnostic features includes signal processing; and

identifying the source and severity of a fault based upon the diagnostic features.

9. A method for monitoring the health-state for electronic equipment, comprising:

measuring current and voltage at an input and an output of the electronic equipment and acquiring data therefrom, wherein said electronic equipment is an electric power converter used to convert power from one form to at least another form;

storing the measured data in a memory

using a processor having access to the measured data, calculating performance metrics for the equipment;

separating the measured data into a plurality of data classes;

generating performance models for at least one data class;

extracting diagnostic features from measured data values by comparing calculated performance metrics with the performance models; and

identifying the source and severity of a fault based upon the diagnostic features.

21. The method according to claim 9 wherein a diagnostic feature extracted from the measured data includes the frequency response of a circuit in the power converter.

22. The method according to claim 21, wherein said frequency response includes at least second order harmonic oscillations.

14. A method for monitoring the health-state for electronic equipment, comprising:

measuring current and voltage at an input and an output of the electronic equipment and acquiring data therefrom, wherein said electronic equipment includes a plurality of electronic circuit components;

storing the measured data in a memory;

using a processor having access to the measured data, calculating performance metrics for the equipment;

separating the measured data into a plurality of data classes;

generating performance models for at least one data class;

extracting diagnostic features from measured data values by comparing calculated performance metrics with the performance models; and

identifying the source and severity of a fault based upon the diagnostic features.

15. The method according to claim 14, wherein the circuit components include digital components.

16. The method according to claim 14, wherein the circuit components include radio-frequency components.

17. The method according to claim 14, wherein at least one of said circuit components includes a radio-frequency component of a global positioning system, and where said performance metrics include at least one parameter selected from the group consisting of:

signal-to-noise-ratio;

bit-error-rate;

cyclic-redundancy-check;

link quality indicator;

received signal strength indication;

frequency offset;

number of satellites; and

dilution of precision.