Systems and Methods for Health Monitoring of Complex Systems

• US 20080312783A1
• Filed: 09/28/2007
• Published: 12/18/2008
• Est. Priority Date: 06/12/2007
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Vian, John L., Mansouri, Ali R., Allen, David, Przytula, Krzysztof Wojtek
• Granted Patent: US 8,437,904 B2
• Time in Patent Office: Days
• US Class Current: 701/29
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 Indicating performance data, e.g. occurrence of a malfunction

Abstract

Systems and methods for health monitoring of complex systems are disclosed. In one embodiment, a method includes receiving a plurality of signals indicative of observation states of plurality of operating variables, performing a combined probability analysis of the plurality of signals using a diagnostic model of a monitored system to provide a health prognosis of the monitored system, and providing an indication of the health prognosis of the monitored system. In some embodiments, the monitored system may be an onboard system of an aircraft.

Claims

1. A method of evaluating a condition of a monitored system, comprising:

receiving a plurality of signals indicative of observation states of a plurality of operating variables, wherein the monitored system includes an onboard system of an aircraft;

performing a combined probability analysis of the plurality of signals using a diagnostic model of the monitored system to provide a health prognosis of the monitored system; and

providing an indication of the health prognosis of the monitored system.

2. The method of claim 1, further comprising predicting a failure of the monitored system based on the health prognosis.

3. The method of claim 1, further comprising, prior to performing a combined probability analysis, developing a diagnostic model of the monitored system that determines a probability of failure based on one or more observation states of the plurality of operating variables.

4. The method of claim 3, wherein the diagnostic model further determines the probability of failure based on knowledge of the monitored system.

5. The method of claim 4, wherein the knowledge of the monitored system includes at least one of a component reliability and a component weighting factor of a component of the monitored system.

6. The method of claim 1, wherein performing a combined probability analysis of the plurality of signals includes performing a combined probability analysis of the plurality of signals using a Bayesian network.

7. The method of claim 6, wherein performing a combined probability analysis includes performing a combined probability analysis to determine a probability of component failure based on a joint probability distribution over the plurality of signals indicative of observation states of the plurality of operating variables.

8. The method of claim 7, wherein the Bayesian network comprises a layered Bayesian network.

9. The method of claim 1, wherein receiving a plurality of signals includes receiving operational data from the monitored aircraft system.

10. The method of claim 1, wherein the onboard system of the aircraft includes an engine bleed pre-cooler of an environmental control system, the method further comprising predicting a failure of the pre-cooler based on a change in at least one of an average deviation in fuel flow, an average deviation in exhaust gas temperature (EGT), and an average deviation in air supply and control system (ASCS) temperature.

11. The method of claim 1 embodied in computer-readable instructions at least one of stored on a computer-readable storage medium and transmitted in real time.

12. A method of evaluating a condition of a monitored system, comprising:

developing at least one diagnostic model configured to determine a probability of failure of the monitored system based on one or more observation states of a plurality of operating variables;

receiving a plurality of signals indicative of observation states of one or more of the plurality of operating variables, wherein the monitored system includes an onboard system of an aircraft;

performing a combined probability analysis using the diagnostic model and at least a portion of the plurality of signals to provide a health prognosis of the monitored system, the health prognosis being indicative of a likelihood of failure of the monitored system; and

providing an indication of the health prognosis of the monitored system.

13. The method of claim 12, wherein the diagnostic model is further configured to determine the probability of failure based on knowledge of the monitored system.

14. The method of claim 12, wherein developing a diagnostic model includes developing a Bayesian network diagnostic model.

15. The method of claim 14, wherein the Bayesian network diagnostic model comprises a layered Bayesian network.

16. A system configured to evaluate a condition of a monitored system, comprising:

an input component configured to receive a plurality of signals indicative of observation states of a plurality of operating variables; and

an analysis component coupled to the input component and configured to

perform a combined probability analysis of the plurality of signals using a diagnostic model of the monitored system to provide a health prognosis of the monitored system, wherein the monitored system includes an onboard system of an aircraft; and

provide an indication of the health prognosis of the monitored system.

17. The system of claim 16, wherein the analysis component is further configured to predict a failure of the monitored system based on the health prognosis.

18. The system of claim 16, wherein the analysis component is further configured to perform the combined probability analysis based on knowledge of the monitored system.

19. The system of claim 16, wherein the analysis component is further configured to perform the combined probability analysis using a Bayesian network.

20. The system of claim 16, wherein the monitored system includes an engine bleed pre-cooler of an aircraft environmental control system, and wherein the analysis component in further configured to predict a failure of the pre-cooler based on a change in at least one of an average deviation in fuel flow, an average deviation in exhaust gas temperature (EGT), and an average deviation in air supply and control system (ASCS) temperature.