Method and apparatus for determining a condition indicator for use in evaluating the health of a component

• US 20030074159A1
• Filed: 12/04/2001
• Published: 04/17/2003
• Est. Priority Date: 05/24/2001
• Status: Active Grant

• Current Assignee: Simmonds Precision Products, Inc. (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: Simmonds Precision Products, Inc. (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Hochmann, David, Bechhoefer, Eric Robert
• Granted Patent: US 6,711,523 B2
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G01H 1/003 of rotating machines G01H1/10 takes precedence
• CPC Class Codes: G01N 2291/2693 Rotor or turbine parts
• CPC Class Codes: G01N 2291/2696 Wheels, Gears, Bearings
• CPC Class Codes: G01N 29/11 by measuring attenuation of acoustic waves
• CPC Class Codes: G01N 29/12 by measuring frequency or resonance of acoustic waves measuring frequency or resonant frequency of mechanical vibrations or acoustic waves in general G01H1/06, G01H3/04, G01H13/00; acoustic resonators G10K11/04; vibration or shock testing of structures G01M7/00
• CPC Class Codes: G01N 29/4445 Classification of defects
• CPC Class Codes: G01N 29/449 Statistical methods not provided for in G01N29/4409, e.g. averaging, smoothing and interpolation
• CPC Class Codes: G01N 29/46 by spectral analysis, e.g. Fourier analysis or wavelet analysis spectral signal processing per se G06F17/14
• CPC Class Codes: G05B 23/0248 Causal models, e.g. fault tree; digraphs; qualitative physics

Abstract

Disclosed are techniques used in connection with determining a health indicator (HI) of a component, such as that of an aircraft component. The HI is determined using condition indicators (CIs) which parameterize characteristics about a component minimizing possibility of a false alarm. Different algorithms are disclosed which may be used in determining one or more CIs. The HI may be determined using a normalized CI value. Techniques are also described in connection with selecting particular CIs that provide for maximizing separation between HI classifications. Given a particular HI at a point in time for a component, techniques are described for predicting a future state or health of the component using the Kalman filter. Techniques are described for estimating data values as an alternative to performing data acquisitions, as may be used when there is no pre-existing data.

Claims

1. A method executed in a computer system for determining a condition indicator about a characteristic of a component comprising:

determining a distribution of observed data associated with said component;

measuring a difference between said distribution and a normal distribution; and

determining said condition indicator using said difference.

2. The method of claim 1, further comprising:

determining whether said distribution of observed data is normally distributed using said difference using at least normality test that is one of;

chi-square goodness of fit test, Kolmogorov-Smirnof goodness of fit test, Lilliefors test of normality and Jarque-Bera test of normality.

3. The method of claim 2, wherein said normal distribution is one of a normal cumulative distribution function and a normal probability distribution function in accordance with said at least one normality test.

4. The method of claim 3, wherein said distribution of observed data associated with a component approximates one of:

a Gaussian distribution if said component is healthy and a non-Gaussian distribution otherwise.

5. The method of claim 2, further comprising:

determining a number of differences between said observed data and expected data, said expected data being represented by said normal distribution; and

determining a sum using said differences; and

if said number of differences is greater than a critical value, determining that said observed data is not normally distributed, said critical value being a threshold.

6. The method of claim 5, further comprising:

determining a score being a maximum deviation from said critical value, said condition indicator being said score.

7. The method of claim 6, wherein sensitivity of said condition indicator increases as a number of observed data values increases.

8. The method of claim 1, wherein said normal distribution approximates a distribution of expected values.

9. A computer program product for determining a condition indicator about a characteristic of a component comprising machine executable code for:

determining a distribution of observed data associated with said component;

measuring a difference between said distribution and a normal distribution; and

determining said condition indicator using said difference.

10. The computer program product of claim 9, further comprising:

machine executable code for determining whether said distribution of observed data is normally distributed using said difference using at least normality test that is one of;

chi-square goodness of fit test, Kolmogorov-Smirnof goodness of fit test, Lilliefors test of normality and Jarque-Bera test of normality.

11. The computer program product of claim 10, wherein said normal distribution is one of a normal cumulative distribution function and a normal probability distribution function in accordance with said at least one normality test.

12. The computer program product of claim 11, wherein said distribution of observed data associated with a component approximates one of:

a Gaussian distribution if said component is healthy and a non-Gaussian distribution otherwise.

13. The computer program product of claim 10, further comprising machine executable code for:

determining a number of differences between said observed data and expected data, said expected data being represented by said normal distribution; and

determining a sum using said differences; and

determining that said observed data is not normally distributed, said critical value being a threshold if said number of differences is greater than a critical value.

14. The computer program product of claim 13, further comprising:

machine executable code for determining a score being a maximum deviation from said critical value, said condition indicator being said score.

15. The computer program product of claim 14, wherein sensitivity of said condition indicator increases as a number of observed data values increases.

16. The computer program product of claim 9, wherein said normal distribution approximates a distribution of expected values.

17. A method executed in a computer system for determining a condition indicator associated with a component, the method comprising:

determining a total impulse signal in accordance with configuration data, said total impulse signal being a superposition of gear and bearing noise represented as a convolution of a gear and bearing signal with a gearbox transfer function; and

determining a condition indicator in accordance with said total impulse signal.

18. The method of claim 17, further comprising:

representing a total impulse signal generated by a configuration of associated with said component as;

[impulse]{circle over (×

)}f(Gear){circle over (×

)}f(Bearing){circle over (×

)}f(Case)≡

[impulse]{circle over (×

)}[f(Gear){circle over (×

)}f(Bearing){circle over (×

)}f(Case)]

in which {circle over (×

)} represents a convolution operation.

19. The method of claim 18, further comprising:

representing convolution operations in a time domain to equivalent operations in a frequency domain.

20. The method of claim 18, further comprising:

estimating [f(Gear){circle over (×

)}f(Bearing){circle over (×

)}f(Case)] as a transfer function in a frequency domain using a linear predictive coding technique to deconvolute a signal into its base components.

21. The method of claim 20, further comprising:

estimating said transfer function, H, in said frequency domain as a/B, wherein a =(al, . . . , an), each ai representing an ith coefficient for an order p, n=p 1, as;

y[n]=α

₁x[n−

1] α

₂x[n−

2] α

₃x[n−

3] . . .

and B is an estimate of an error represented as;

B=Σ

_allb

in which;

b=(y−

y_hat)², y=y[1, 2, . . . n],

y_hat is an estimated value of y, y_hat=ax,

x is a time delayed signal represented as;

$x=[\text{\hspace{1em}<br><br>}<br><br>\begin{array}{c}x<br><br>\left[n-1,n-2,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>n-p\right]\\ x<br><br>\left[n-2,n-3,n-p-1\right]\\ \mathrm{⋮<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{⋮<br><br>}\end{array}]$

where a (x^T x)<sup>−

1</sup>x^Ty, values for al . . . an.

22. The method of claim 21, further comprising:

estimating an impulse, IMP, in said frequency domain of said component as;

IMP=exp(log(Y)−

log(H)),

in which;

Y=fft(y) and H=fft(h), where fft is the Fourier transform function, y and h are in a time domain, Y and H are in said frequency domain.

23. The method of claim 22, wherein a value associated with H increases as a fault increases.

24. The method of claim 22, wherein said condition indicator is said value of IMP.

25. The method of claim 22, further comprising:

calculating a power spectral density of said impulse IMP in a frequency domain; and

determining a value of said power spectral density at a frequency of interest, said condition indicator being said value.

26. The method of claim 25, wherein said frequency of interest is at least one of:

a bearing passing frequency for a bearing fault, and a mesh frequency for a gear fault.

27. The method of claim 26, further comprising:

performing a Fourier transformation to obtain IMP in said frequency domain.

28. The method of claim 17, further comprising:

detecting a fault in connection with predetermined values of said health status using said condition indicator, wherein said fault being detected is one of a pit and spall on one of;

a gear tooth, inner bearing race, outer bearing race, and bearing roller element.

29. A computer program product for determining a condition indicator associated with a component, the computer program product comprising machine executable code for:

determining a total impulse signal in accordance with configuration data, said total impulse signal being a superposition of gear and bearing noise represented as a convolution of a gear and bearing signal with a gearbox transfer function; and

determining a condition indicator in accordance with said total impulse signal.

30. The computer program product of claim 29, further comprising machine executable code for:

representing a total impulse signal generated by a configuration of associated with said component as;

[impulse]{circle over (×

)}f(Gear){circle over (×

)}f(Bearing){circle over (×

)}f(Case)≡

[impulse]{circle over (×

)}[f(Gear){circle over (×

)}f(Bearing){circle over (×

)}f(Case)]

in which {circle over (×

)} represents a convolution operation.

31. The computer program of claim 30, further comprising machine executable code for:

representing convolution operations in a time domain to equivalent operations in a frequency domain.

32. The computer program product of claim 30, further comprising machine executable code for:

estimating [f(Gear){circle over (×

)}f(Bearing){circle over (×

)}f(Case)] as a transfer function in a frequency domain using a linear predictive coding technique to deconvolute a signal into its base components.

33. The computer program product of claim 32, further comprising machine executable code for:

estimating said transfer function, H, in said frequency domain as a/B, wherein a=(al, . . . , an), each ai representing an ith coefficient for an order p, n=p 1, as;

y[n]=α

₁x[n−

1 α

₂x[n−

2] α

₃x[n−

3] . . .

and B is an estimate of an error represented as;

$B=\underset{\mathrm{all}}{\sum <br><br>}<br><br>b$

in which;

b=(y−

y_hat)², y=y[1, 2, . . . n],

y_hat is an estimated value of y, y_hat=ax,

x is a time delayed signal represented as;

$x=[\text{\hspace{1em}<br><br>}<br><br>\begin{array}{c}x<br><br>\left[n-1,n-2,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>n-p\right]\\ x<br><br>\left[n-2,n-3,n-p-1\right]\\ \mathrm{⋮<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{⋮<br><br>}\end{array}]$

where a=(x^Tx)<sup>−

1</sup>x^Ty, values for a1 . . . an.

34. The computer program product of claim 33, further comprising machine executable code for:

estimating an impulse, IMP, in said frequency domain of said component as;

IMP=exp(log(Y)−

log(H)),

in which;

Y=fft(y) and H=fft(h), where fft is the Fourier transform function, y and h are in a time domain, Y and H are in said frequency domain.

35. The computer program product of claim 34, wherein a value associated with H increases as a fault increases.

36. The computer program product of claim 34, wherein said condition indicator is said value of IMP.

37. The computer program product of claim 34, further comprising machine executable code for:

calculating a power spectral density of said impulse IMP in a frequency domain; and

determining a value of said power spectral density at a frequency of interest, said condition indicator being said value.

38. The computer program product of claim 37, wherein said frequency of interest is at least one of:

a bearing passing frequency for a bearing fault, and a mesh frequency for a gear fault.

39. The computer program product of claim 38, further comprising machine executable code for

performing a Fourier transformation to obtain IMP in said frequency domain.

40. The computer program product of claim 39, further comprising machine executable code for:

detecting a fault in connection with predetermined values of said health status using said condition indicator, wherein said fault being detected is one of a pit and spall on one of;

a gear tooth, inner bearing race, outer bearing race, and bearing roller element.

41. A method executed in a computer system for determining a health status of a component using at least one condition indicator, the method comprising:

determining said at least one condition indicator using at least one of;

an impulse determination technique and a statistical normality test; and

determining said health indicator in accordance with said at least one condition indicator.

42. The method of claim 41, wherein said statistical normality test is one of:

chi-square goodness of fit test, Kohnogorov-Smimof goodness of fit test, Lilliefors test of normality and Jarque-Bera test of normality.

43. The method of claim 41, wherein expected data values approximate a normal distribution.

44. A computer program product for determining a health status of a component using at least one condition indicator, the method comprising:

determining said at least one condition indicator using at least one of;

an impulse determination technique and a statistical normality test; and

determining said health indicator in accordance with said at least one condition indicator.

45. The computer program product of claim 44, wherein said statistical normality test is one of:

chi-square goodness of fit test, Kolmogorov-Smimof goodness of fit test, Lilliefors test of normality and Jarque-Bera test of normality.

46. The computer program product of claim 44, wherein expected data values approximate a normal distribution.