Quantification of condition indicators in the presence of synchronous noise

• US 8,380,447 B2
• Filed: 04/14/2010
• Issued: 02/19/2013
• 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
• Primary Examiner(s): Teixeira Moffat, Jonathan C
• Assistant Examiner(s): SUN, XIUQIN
• Application Number:
  US12/798,938
• Publication Number:
  US 20110257901A1
• Time in Patent Office: 1,042 Days
• Field of Search: 702/34, 702/35, 702/181, 702/185, 702/191, 702/195
• 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 acquiring a plurality of measured values corresponding to the condition indicators. Synchronous noise components are removed from the measured values to provide revised measured values, and the revised measured values are used to determine noise-filtered condition indicator values. The noise-filtered condition indicator values may be used in algorithms to determine the health of the component. Removing synchronous noise components may include 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;

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<sup>−

1</sup> and where R<sup>−

1</sup> is the inverse of R;

estimating the synchronous noise components 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 components by subtracting a^tx(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<sup>−

1</sup> and where R<sup>−

1</sup> is the inverse of R;

executable code that estimates the synchronous noise components 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 components by subtracting a^tx(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.