METHOD FOR REAL-TIME MODEL BASED STRUCTURAL ANOMALY DETECTION

• US 20130073141A1
• Filed: 09/19/2011
• Published: 03/21/2013
• Est. Priority Date: 09/19/2011
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: Eric R. Reichenbach, James M. Urnes Sr, Timothy A. Smith
• Inventors: Urnes, James M. Sr., Smith, Timothy A., Reichenbach, Eric R.
• Granted Patent: US 9,020,689 B2
• Time in Patent Office: Days
• US Class Current: 701/32.9
• CPC Class Codes: B64D 2045/0085 Devices for aircraft health monitoring, e.g. monitoring flutter or vibration
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: Y02T 90/00 Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.

Claims

1. A method for real-time model based vehicle structural anomaly detection, comprising:

receiving a real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle;

comparing the real-time measurement to expected operation data for the location to provide a modeling error signal;

calculating a statistical significance of the modeling error signal to provide an error significance;

determining a persistence of the error significance; and

indicating a structural anomaly, if the persistence exceeds a persistence threshold value.

2. The method of claim 1, wherein the step of calculating the statistical significance of the modeling error signal further comprises:

recursively estimating an estimated mean and an estimated variance of the modeling error signal to determine whether the modeling error signal warrants indicating the structural anomaly; and

assessing a probability that the modeling error signal is significantly away from zero by computing a Probability of False Alarm (Pfa) of the modeling error signal based on the estimated mean, and the estimated variance to provide the error significance.

3. The method of claim 1, wherein the step of determining the persistence of the error significance further comprises:

inputting a unity signal into a first order filter when the error significance falls below a Pfa threshold value;

comparing an output of the first order filter to a value close to one; and

indicating a structural anomaly condition when the output of the first order filter is sufficiently close to one, wherein the persistence is high.

4. The method of claim 3, wherein the Pfa threshold value and a filter time constant of the first order filter are tunable parameters that depend on a quality of the modeling error signal and tolerance for false positive indications.

5. The method of claim 1, further comprising activating a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated.

6. The method of claim 5, wherein the step of activating the control mechanism comprises at least one member selected from the group consisting of:

a control surface actuation, a lift surface actuation, a flow control actuation, actuation of shaped memory alloys, actuation via active structural materials, and a propulsive power alteration.

7. The method of claim 1, further comprising installing a plurality of measurement sensors on the vehicle structure operable to measure the real-time measurement.

8. The method of claim 1, further comprising:

reading a sensor signal during a healthy operation of the vehicle; and

formulating an expected signature signal response for a healthy operation of the vehicle based on the sensor signal to provide the expected operation data.

9. The method of claim 1, further comprising gathering a representative sensor signal during further operation of the vehicle on a periodic basis to obtain the real-time measurement.

10. The method of claim 1, wherein the structural anomaly comprise at least one member selected from the group consisting of:

an in-flight operation, a stress from wind shear on a lift surface, a stress from a debris impact on a lift surface, a stress from a gust on a lift surface, a vibration on a wing, a flutter on a wing, a fuselage flexure, an excessive bending of the fuselage, a propulsion system anomaly, an excessive linear displacement, an excessive angular displacement, a structural fatigue, a control surface anomaly, and a lift surface anomaly.

11. A real-time model based structural anomaly detection system, comprising:

a structural anomaly detection module operable to;

receive a real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle;

compare the real-time measurement to expected operation data for the location to provide a modeling error signal;

calculate a statistical significance of the modeling error signal to provide an error significance;

determine a persistence of the error significance if the error significance is below a selected Pfa threshold value; and

indicate a structural anomaly, if the persistence exceeds a persistence threshold value; and

an anomaly mitigation module operable to activate a control mechanism to compensate for the structural anomaly, if the structural anomaly is indicated.

12. The system of claim 11, further comprising:

a mean/variance estimator operable to recursively estimate an estimated mean and an estimated variance of the modeling error signal;

an error function module operable to assess a probability that the modeling error signal is significantly away from zero by computing a Probability of False Alarm (Pfa) of the modeling error signal based on the estimated mean, and the estimated variance to obtain the error significance; and

a smoother comprising a first order filter and operable to declare an anomaly condition when an output of the first order filter is sufficiently close to one indicating the error significance is persistently high.

13. The system of claim 12, wherein the first order filter comprises the selected Pfa threshold value and a filter time constant that are tunable parameters based on quality of the modeling error signal and tolerance for false positive indications.

14. The system of claim 11, wherein the vehicle is an aircraft and the step of activating the control mechanism comprises at least one member selected from the group consisting of:

a control surface actuation, a lift surface actuation, a flow control actuation, actuation of shaped memory alloys, actuation via active structural materials, and a propulsive power alteration.

15. The system of claim 11, further comprising a healthy structure model formulation module operable to:

read a sensor signal during a healthy operation of the vehicle;

store the sensor signal in a memory;

formulate an expected signature signal response for a healthy operation of the vehicle based on the sensor signal to obtain the expected operation data; and

provide the expected operation data to the structural anomaly detection module.

16. The system of claim 11, further comprising a real-time measurement module operable to:

measure a representative sensor signal during further operation of the vehicle on a periodic basis to obtain the real-time measurement; and

provide the real-time measurement to the structural anomaly detection module.

17. The system of claim 11, further comprising a plurality of sensors comprising at least one member selected from the group consisting of:

a strain sensor, a vibration sensor, a noise sensor, a temperature sensor, and an optic sensor.

18. The system of claim 11, wherein the structural anomaly comprise at least one member selected from the group consisting of:

an in-flight operation, a stress from wind shear on a lift surface, a stress from a debris impact on a lift surface, a stress from a gust on a lift surface, a vibration on a wing, a flutter on a wing, a fuselage flexure, an excessive bending of a fuselage, a propulsion system anomaly, an excessive linear displacement, an excessive angular displacement, a structural fatigue, a control surface anomaly, and a lift surface anomaly.

19. A method for alleviating a structural anomaly, comprising:

obtaining a modeling error signal of a structure;

assessing a probability that the modeling error signal is significantly away from zero by computing a Probability of False Alarm (Pfa) to provide an error significance;

inputting a unity signal to a first order filter when the error significance falls below a Pfa threshold value; and

indicating a structural anomaly condition when an output of the first order filter is sufficiently close to one.

20. The method of claim 19, further comprising activating a control mechanism to compensate for the structural anomaly condition, if the structural anomaly condition is indicated.