Failure detection system

• US 6,298,316 B1
• Filed: 05/14/1999
• Issued: 10/02/2001
• Est. Priority Date: 05/18/1998
• Status: Expired due to Term

• Current Assignee: Northrop Grumman Systems Corporation (Northrop Grumman Corporation)
• Original Assignee: Litton Systems Incorporated
• Inventors: Diesel, John W.
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): Raymond, Edward
• Application Number:
  US09/312,200
• Time in Patent Office: 872 Days
• Field of Search: 702/190, 714/2, 714/25, 714/39, 714/40, 714/41, 714/47, 714/48, 714/49
• US Class Current: 702/190
• CPC Class Codes: G01S 19/23 Testing, monitoring, correcting or calibrating of receiver elements
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

The invention is a method and apparatus for determining a failure in a subsystem that is part of a system, the system being characterized in terms of a state vector comprising a plurality of state variables. The state vector is updated at times (jK+k)TΔt using one or more Kalman filter processes, j taking on integer values, K being a predetermined integer, k taking on integer values between 1 and K for each value of j, T being a predetermined integer, and Δt being a predetermined time interval. The method comprises the steps: (a) executing at present time a plurality of Kalman filter processes where one of the Kalman filter processes determines the state vector at present time minus (K−1)TΔt using data from subsystems without failures; (b) determining the values of one or more statistical measures of one or more residuals for each of one or more Kalman filter processes for one or more time periods equal to or greater than TΔt; and (c) determining a subsystem failure from the one or more values of the statistical measures.

Claims

1. A method for determining a failure in a subsystem that is part of a system, the system being characterized in terms of a state vector comprising a plurality of state variables, the state vector being updated at times (jK+k)TΔ

t using one or more Kalman filter processes, j taking on integer values, K being a predetermined integer, k taking on integer values between 1 and K for each value of j, T being a predetermined integer, Δ

t being a predetermined time interval, the method comprising the steps;

(a) executing at present time a plurality of Kalman filter processes, one of the Kalman filter processes determining the state vector at present time minus (K−

1)TΔ

t using data from subsystems without failures;

(b) determining the values of one or more statistical measures of one or more residuals for each of one or more Kalman filter processes for one or more time periods equal to or greater than TΔ

t;

(c) determining a subsystem failure from the one or more values of the statistical measures.

2. The method of claim 1 wherein step (a) comprises the step:

(a1) executing an (M+2)'"'"'th Kalman filter process at present time to obtain the error-state vector and associated covariance matrix for present time minus (K−

1)TΔ

t utilizing data resulting from the prior execution of the (M+2)'"'"'th Kalman filter process, M being a predetermined integer, the (M+2)'"'"'th Kalman filter process utilizing data from only those subsystems without failures during the period from present time minus KTΔ

t to present time minus TΔ

t.

3. The method of claim 2 wherein the measurement vector utilized by the (M+2)'"'"'th Kalman filter process is obtained by modifying a measurement vector utilized by an (M+1)'"'"'th Kalman filter process.

4. The method of claim 1 wherein step (a) comprises the step:

(a1) executing an (M+1)'"'"'th Kalman filter process at present time to obtain the error-state vector and associated covariance matrix for times equal to present time minus (K−

k)TΔ

t utilizing data resulting from the prior execution of the (M+1)'"'"'th Kalman filter process at present time minus TΔ

t, M being a predetermined integer, the (M+1)'"'"'th Kalman filter process utilizing data from subsystems available and operable during the period from present time minus KTΔ

t to present time minus TΔ

t .

5. The method of claim 4 wherein the (M+1)'"'"'th Kalman filter process utilizes the error-state vector resulting from the execution of an (M+2)'"'"'th Kalman filter process and extrapolated to present time minus (K−

1)TΔ

t to obtain an updated error-state vector for present time minus (K−

1)TΔ

t.

6. The method of claim 4 wherein the measurement vector utilized by the (M+1)'"'"'th Kalman filter process for present time minus (K−

k)TΔ

t is obtained by modifying the measurement vector utilized by the (M+1)'"'"'th Kalman filter process during a present time minus TΔ

t execution for present time minus (K−

k)TΔ

t.

7. The method of claim 1 wherein step (a) comprises the steps:

(a1) executing M Kalman filter processes for testing M subsystems comprising a system, the m'"'"'th Kalman filter process providing information concerning failures in the m'"'"'th subsystem, the number of subsystems in a system being equal to or greater than M, the error-state vector including one or more components associated with each subsystem, the error-state vector components associated with the m'"'"'th subsystem being called the m-components, the values of the diagonal elements of the process noise covariance matrix corresponding to the m-components being set to values sufficiently high as to make the effect of the values of the m-components on the values of the other error-state components negligible.

8. The method of claim 7 wherein the m'"'"'th Kalman filter process is executed at present time to obtain the error-state vector and associated covariance matrix for present time utilizing data resulting from the prior execution of the m'"'"'th Kalman filter process at present time minus TΔ

t, the M Kalman filter processes utilizing data from subsystems available and operable during the period from present time minus KTΔ

t to present time minus TΔ

t.

9. The method of claim 7 wherein the measurement vector utilized by the m'"'"'th Kalman filter process is obtained by modifying the measurement vector utilized by an (M+1)'"'"'th Kalman filter process during a present time minus TΔ

t execution for present time minus TΔ

t.

10. The method of claim 1 wherein at least one statistical measure is the average.

11. The method of claim 1 wherein step (c) comprises the steps:

(c1) determining that there are no subsystem failures;

otherwise,

(c2) determining the subsystem having a failure.

12. The method of claim 11 wherein an (M+1)'"'"'th Kalman filter process is executed at present time to obtain the error-state vector and associated covariance matrix for times equal to present time minus (K−

k)TΔ

t utilizing data resulting from the prior execution of the (M+1)'"'"'th Kalman filter process at present time minus TΔ

t, M being a predetermined integer, the (M+1)'"'"'th Kalman filter process utilizing data from subsystems available and operable during the period from present time minus KTΔ

t to present time minus TΔ

t , the determination that there are no subsystem failures being made if the values of one or more statistical measures do not exceed respectively one or more predetermined threshold levels.

13. The method of claim 1 wherein M Kalman filter processes are executed for testing M subsystems comprising a system, the m'"'"'th Kalman filter process providing information concerning failures in the m'"'"'th subsystem, the number of subsystems in a system being equal to or greater than M, the error-state vector including one or more components associated with each subsystem, the error-state vector components associated with the m'"'"'th subsystem being called the m-components, the values of the diagonal elements of the process noise covariance matrix corresponding to the m-components being set to values sufficiently high as to make the effect of the values of the m-components on the values of the other error-state components negligible, the subsystem having a failure being associated with the Kalman filter process for which the values of one or more statistical measures respectively exceed one or more predetermined thresholds.

14. Apparatus for practicing the method of claim 1.

15. Apparatus for determining a failure in a subsystem that is part of a system, the system being characterized in terms of a state vector comprising a plurality of state variables, the state vector being updated at times (jK+k)TΔ

t using one or more Kalman filter processes, j taking on integer values, K being a predetermined integer, k taking on integer values between 1 and K for each value of j, T being a predetermined integer, Δ

t being a predetermined time interval, the method comprising the steps;

a first processor for executing at present time a plurality of Kalman filter processes, one of the Kalman filter processes determining the state vector at present time minus (K−

1)TΔ

t using data from subsystems without failures;

a second processor for determining the values of one or more statistical measures of one or more residuals for each of one or more Kalman filter processes for one or more time periods equal to or greater than TΔ

t;

a third processor for determining a subsystem failure from the one or more values of the statistical measures.

16. The apparatus of claim 15 wherein the first processor comprises:

a means for executing an (M+2)'"'"'th Kalman filter process at present time to obtain the error-state vector and associated covariance matrix for present time minus (K−

1)TΔ

t utilizing data resulting from the prior execution of the (M+2)'"'"'th Kalman filter process, M being a predetermined integer, the (M+2)'"'"'th Kalman filter process utilizing data from only those subsystems without failures during the period from present time minus KTΔ

t to present time minus TΔ

t.

17. The apparatus of claim 16 wherein the measurement vector utilized by the (M+2)'"'"'th Kalman filter process is obtained by modifying a measurement vector utilized by an (M+1)'"'"'th Kalman filter process.

18. The apparatus of claim 15 wherein the first processor comprises:

a means for executing an (M+1)'"'"'th Kalman filter process at present time to obtain the error-state vector and associated covariance matrix for times equal to present time minus (K−

k)TΔ

t utilizing data resulting from the prior execution of the (M+1)'"'"'th Kalman filter process at present time minus TΔ

t, M being a predetermined integer, the (M+1)'"'"'th Kalman filter process utilizing data from subsystems available and operable during the period from present time minus KTΔ

t to present time minus TΔ

t.

19. The apparatus of claim 18 wherein the (M+1)'"'"'th Kalman filter process utilizes the error-state vector resulting from the execution of an (M+2)'"'"'th filter process and extrapolated to present time minus (K−

1)TΔ

t to obtain an updated error- state vector for present time minus (K−

1)TΔ

t.

20. The apparatus of claim 18 wherein the measurement vector utilized by the (M+1)'"'"'th Kalman filter process for present time minus (K−

k)TΔ

t is obtained by modifying the measurement vector utilized by the (M+1)'"'"'th Kalman filter process during a present time minus TΔ

t execution for present time minus (K−

k)TΔ

t.

21. The apparatus of claim 15 wherein the first processor comprises:

a means for executing M Kalman filter processes for testing M subsystems comprising a system, the m'"'"'th Kalman filter process providing information concerning failures in the m'"'"'th subsystem, the number of subsystems in a system being equal to or greater than M, the error-state vector including one or more components associated with each subsystem, the error-state vector components associated with the m'"'"'th subsystem being called the m-components, the values of the diagonal elements of the process noise covariance matrix corresponding to the m-components being set to values sufficiently high as to make the effect of the values of the m-components on the values of the other error-state components negligible.

22. The apparatus of claim 21 wherein the m'"'"'th Kalman filter process is executed at present time to obtain the error-state vector and associated covariance matrix for present time utilizing data resulting from the prior execution of the m'"'"'th Kalman filter process at present time minus TΔ

t, the M Kalman filter processes utilizing data from subsystems available and operable during the period from present time minus KTΔ

t to present time minus TΔ

t.

23. The apparatus of claim 21 wherein the measurement vector utilized by the m'"'"'th Kalman filter process is obtained by modifying the measurement vector utilized by an (M+1)'"'"'th Kalman filter process during a present time minus TΔ

t execution for present time minus TΔ

t.

24. The apparatus of claim 15 wherein at least one statistical measure is the average.

25. The apparatus of claim 15 wherein step the third processor comprises:

a means for determining that there are no subsystem failures;

otherwise,

a means for determining the subsystem having a failure.

26. The apparatus of claim 25 wherein an (M+1)'"'"'th Kalman filter process is executed at present time to obtain the error-state vector and associated covariance matrix for times equal to present time minus (K−

k)TΔ

t utilizing data resulting from the prior execution of the (M+1)'"'"'th Kalman filter process at present time minus TΔ

t, M being a predetermined integer, the (M+1)'"'"'th Kalman filter process utilizing data from subsystems available and operable during the period from present time minus KTΔ

t to present time minus TΔ

t, the determination that there are no subsystem failures being made if the values of one or more statistical measures do not exceed respectively one or more predetermined threshold levels.

27. The apparatus of claim 15 wherein M Kalman filter processes are executed for testing M subsystems comprising a system, the m'"'"'th Kalman filter process providing information concerning failures in the m'"'"'th subsystem, the number of subsystems in a system being equal to or greater than M, the error-state vector including one or more components associated with each subsystem, the error-state vector components associated with the m'"'"'th subsystem being called the m-components, the values of the diagonal elements of the process noise covariance matrix corresponding to the m-components being set to values sufficiently high as to make the effect of the values of the m-components on the values of the other error-state components negligible, the subsystem having a failure being associated with the Kalman filter process for which the values of one or more statistical measures respectively exceed one or more predetermined thresholds.