Granted Patent B2

US 8,380,447 · App. 12/798,938 · Granted Feb 19, 2013

Abstract

Claims (45)

1. A method of determining health of a component, comprising:

using at least one processor to perform processes including:

acquiring a plurality of measured values for the component corresponding to condition indicators;

removing synchronous noise components from the measured values to provide revised measured values; and

using the revised measured values to determine noise-filtered condition indicator values;

determining a health indicator value using the noise-filtered condition indicator values;

mapping a plurality of condition indicator functions for the noise-filtered condition indicator values into a health indicator function for the health indicator value;

determining probability distribution functions for the condition indicator functions; and

determining a probability distribution function for the health indicator function based on the probability distribution functions for the condition indicator functions.

2. The method, according to claim 1 , wherein removing synchronous noise components includes determining a predicted value of the synchronous noise components using covariance and autocovariance of the measured values.

3. The method, according to claim 1 , wherein removing synchronous noise components from the measured values to provide the revised measured values includes:

determining an autocovariance vector r where r=x(n−D)·x(n) and where x represents the measured values and D represents a delay;

determining a covariance vector R where R=x(n−D)·x(n−D) and where x represents the measured values and D represents a delay;

determining a vector a where a=−r·R −1 and where R −1 is the inverse of R;

estimating the synchronous noise components using a t x(n−D), where a t represents the transpose of the a vector and D represents a delay; and

removing the synchronous noise components by subtracting a t x(n−D) from x(n) to provide a residual signal that captures impulse signals indicative of component fault.

4. The method, according to claim 3 , wherein D is selected from the group consisting of one or two.

5. The method, according to claim 1 , wherein the probability distribution function for the health indicator function is determined mathematically.

6. The method, according to claim 1 , wherein the condition indicator functions are made independent by dividing each of the condition indicator functions by an inverse covariance thereof.

7. The method, according to claim 6 , wherein the condition indicator functions are at least one of: Rayleigh distribution functions and Gaussian distribution functions.

8. The method, according to claim 1 , wherein the component is one of: a split torque gearbox and a planetary based gearbox.

9. The method, according to claim 1 , wherein the probability distribution function for the health indicator function has a Nakagami distribution.

10. The method, according to claim 1 , wherein the condition indicator functions are correlated.

11. A non-transitory computer readable storage medium storing software that, when executed by at least one processor, determines health of a component, the software comprising:

executable code that acquires a plurality of measured values for the component corresponding to condition indicators;

executable code that removes synchronous noise components from the measured values to provide revised measured values; and

executable code that uses the revised measured values to determine noise-filtered condition indicator values;

executable code that determines a health indicator value using the noise-filtered condition indicator values;

executable code that maps a plurality of condition indicator functions for the noise-filtered condition indicator values into a health indicator function for the health indicator value;

executable code that determines probability distribution functions for the condition indicator functions; and

executable code that determines a probability distribution function for the health indicator function based on the probability distribution functions for the condition indicator functions.

12. The non-transitory computer readable storage medium, according to claim 11 , wherein the executable code that removes synchronous noise components includes executable code that determines a predicted value of the synchronous noise components using covariance and autocovariance of the measured values.

13. The non-transitory computer readable storage medium, according to claim 11 , wherein the executable code that removes the synchronous noise components includes:

executable code that determines an autocovariance vector r where r=x(n−D)·x(n) and where x represents the measured values and D represents a delay;

executable code that determines a covariance vector R where R=x(n−D)·x(n−D) and where x represents the measured values and D represents a delay;

executable code that determines a vector a where a=−r·R −1 and where R −1 is the inverse of R;

executable code that estimates the synchronous noise components using a t x(n−D), where a t represents the transpose of the a vector and D represents a delay; and

executable code that removes the synchronous noise components by subtracting a t x(n−D) from x(n) to provide a residual signal that captures impulse signals indicative of component fault.

14. The non-transitory computer readable storage medium, according to claim 13 , wherein D is selected from the group consisting of one or two.

15. The non-transitory computer readable storage medium, according to claim 11 , wherein the probability distribution function for the health indicator function is determined mathematically.

16. The non-transitory computer readable storage medium, according to claim 11 , wherein the condition indicator functions are made independent by dividing each of the condition indicator functions by an inverse covariance thereof.

17. The non-transitory computer readable storage medium, according to claim 16 , wherein the condition indicator functions are at least one of: Rayleigh distribution functions and Gaussian distribution functions.

18. The non-transitory computer readable storage medium, according to claim 11 , wherein the component is one of: a split torque gearbox and a planetary based gearbox.

19. The non-transitory computer readable storage medium, according to claim 11 , wherein the probability distribution function for the health indicator function has a Nakagami distribution.

20. The non-transitory computer readable storage medium, according to claim 11 , wherein the condition indicator functions are correlated.

Assignments (9)