Quantification of condition indicators in the presence of synchronous noise

• US 20110257901A1
• Filed: 04/14/2010
• Published: 10/20/2011
• Est. Priority Date: 04/14/2010
• Status: Active Grant

• Current Assignee: Simmonds Precision Products, Inc. (Rtx Corporation)
• Original Assignee: Simmonds Precision Products, Inc. (Rtx Corporation)
• Inventors: Bechhoefer, Eric Robert
• Granted Patent: US 8,380,447 B2
• Time in Patent Office: Days
• US Class Current: 702/34
• CPC Class Codes: G01H 1/00 Measuring characteristics of vibrations in solids by using direct conduction to the detector G01H9/00, G01H11/00 take precedence

Abstract

Determining health of a component includes using at least one processor to perform processes including mapping a plurality of condition indicator functions for condition indicator values into a health indicator function for the health indicator value, determining a probability distribution function for the health indicator function based on the probability distribution functions for the condition indicator functions, acquiring a plurality of measured values corresponding to the condition indicator values, removing synchronous noise components from the measured values to provide revised measured values, and using the revised measured values to determine condition indicator values. Removing synchronous noise components may includes determining a predicted value thereof using covariance and autocovariance of the measured values. The component may be a split torque gearbox.

Claims

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

using at least one processor to perform processes including;

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

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

acquiring a plurality of measured values corresponding to the condition indicator values;

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

using the revised measured values to determine condition indicator values.

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

3. The method, according to claim 2, further comprising:

determining an autocovariance vector r where r=x(n−

D)·

x(n) and where x represents the measured data and D represents a delay;

determining a covariance vector R where R=x(n−

D)·

x(n−

D) and where x represents the measured data and D represents a delay;

determining a vector a where a=−

r·

R<sup>−

1</sup> and where R<sup>−

1</sup> is the inverse of R;

estimating the synchronous noise using a^tx(n−

D), where a^t represents the transpose of the a vector and D represents a delay; and

removing the synchronous noise 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 computer readable storage medium storing software that determines health of a component, a plurality of condition indicator functions for condition indicator values being mapped into a health indicator function for the health indicator value, the software comprising:

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

executable code that acquires a plurality of measured values corresponding to the condition indicator values;

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 condition indicator values.

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

13. The computer readable storage medium, according to claim 12, further comprising:

executable code that determines an autocovariance vector r where r=x(n−

D)·

x(n) and where x represents the measured data 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 data and D represents a delay;

executable code that determines a vector a where a=−

r·

R<sup>−

1</sup> and where R<sup>−

1</sup> is the inverse of R;

executable code that estimates the synchronous noise using a^tx(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 by subtracting a^tx(n−

D) from x(n) to provide a residual signal that captures impulse signals indicative of component fault.

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

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

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

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