Method and apparatus for trending and predicting the health of a component

• US 6,651,012 B1
• Filed: 12/04/2001
• Issued: 11/18/2003
• Est. Priority Date: 05/24/2001
• Status: Expired due to Term

• 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: Bechhoefer, Eric Robert
• Primary Examiner(s): Barlow, John
• Assistant Examiner(s): BHAT, ADITYA S
• Application Number:
  US10/011,622
• Time in Patent Office: 714 Days
• Field of Search: 702/190, 702/34, 701/300, 382/103, 455/431
• US Class Current: 702/34
• 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 an health indicator of a component at a subsequent time comprising:

determining a first health indicator of said component at a time, n, in accordance with at least one corresponding condition indicator; and

using a three state Kalman filter to determine a second health indicator of said component at a time subsequent to time n, wherein one of said states of said three state Kalman filter is an estimated filtered health indicator of said first health indicator.

2. The method of claim 1, further comprising:

estimating said first health indicator using a hypothesis determination technique.

3. The method of claim 2, wherein the health of a component at a first time is associated with a third health indicator, and at a second time occurring a predetermined amount of time after the first time, the health of the component is associated with a fourth health indicator, said third indicator and said fourth indicator being classified as varying degrees of a same state of health of the component for said predetermined amount of time.

4. The method of claim 1, wherein said states of said three state Kalman filter include a rate of change of health and acceleration of health condition.

5. The method of claim 4, wherein said rate of change of health is a first derivative of said estimated filtered health indicator and said acceleration is a second derivative of said estimated filtered health indicator.

6. The method of claim 5, further comprising:

determining said second health indicator using at least one of;

said first derivative of said estimated filtered health indicator and said second derivative of said estimated filtered health indicator.

7. The method of claim 1, wherein at least one condition indicator is a normalized condition indicator.

8. The method of claim 1, wherein $H=\left[\begin{array}{ccc}1& 0& 0\end{array}\right]$$\Phi$

=[1dtdt2201dt001]$Q=2<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\sigma <br><br>}}^2<br><br>\stackrel{\_}{t}<br><br>\left[\begin{array}{ccc}\frac{{\mathrm{dt}}^3}{2}& \frac{{\mathrm{dt}}^2}{2}& \stackrel{\_}{t}\\ \frac{{\mathrm{dt}}^2}{2}& \mathrm{dt}& 1\\ \stackrel{\_}{t}& 1& \frac{1}{\stackrel{\_}{t}}\end{array}\right]$$X=\left[\begin{array}{c}\mathrm{HI\_est}\\ \stackrel{.}{H}<br><br>I\\ \stackrel{\mathrm{¨<br><br>}}{H}<br><br>I\end{array}\right]$

in which;

σ

is a power spectral density,

R is a measurement error,

P is a covariance,

Q is a plant noise,

H is a measurement matrix,

K is a Kalman gain,

Φ

is state transition matrix,

{dot over (H)} is a first derivative of HI_est, and

{umlaut over (H)} is a second derivative of HI_est and

9. A method executed in a computer system for determining an health indicator of a component at a subsequent time comprising:

determining a first health indicator of said component at a time, n, in accordance with at least one corresponding condition indicator;

using a three state Kalman filter to determine a second health indicator of said component at a time subsequent to time n; and

estimating said first health indicator using a hypothesis determination technique, wherein said at least one condition indicator is a normalized condition indicator.

10. A method executed in a computer system for determining an health indicator of a component at a subsequent time comprising:

determining a first health indicator of said component at a time, n, in accordance with at least one corresponding condition indicator; and

using a three state Kalman filter to determine a second health indicator of said component at a time subsequent to time n, wherein $H=\left[\begin{array}{ccc}1& 0& 0\end{array}\right]$$\mathrm{\Phi <br><br>}=\left[\begin{array}{ccc}1& \mathrm{dt}& \frac{{\mathrm{dt}}^2}{2}\\ 0& 1& \mathrm{dt}\\ 0& 0& 1\end{array}\right]$$Q=2<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\sigma <br><br>}}^2<br><br>\stackrel{\_}{t}<br><br>\left[\begin{array}{ccc}\frac{{\mathrm{dt}}^3}{2}& \frac{{\mathrm{dt}}^2}{2}& \stackrel{\_}{t}\\ \frac{{\mathrm{dt}}^2}{2}& \mathrm{dt}& 1\\ \stackrel{\_}{t}& 1& \frac{1}{\stackrel{\_}{t}}\end{array}\right]$$X=\left[\begin{array}{c}\mathrm{HI\_est}\\ \stackrel{.}{H}<br><br>I\\ \stackrel{\mathrm{¨<br><br>}}{H}<br><br>I\end{array}\right]$

in which;

σ

is a power spectral density,

R is a measurement error,

P is a covariance,

Q is a plant noise,

H is a measurement matrix,

K is a Kalman gain and

Φ

is state transition matrix, and

 

X<sub>t|t−

1</sub>=Φ

X<sub>t−

1|t−

1</sub> 

 

(Equation T1)

11. A computer program product for determining an health indicator of a component at a subsequent time comprising machine executable code for:

determining a first health indicator of said component at a time, n, in accordance with at least one corresponding condition indicator; and

using a three state Kalman filter to determine a second health indicator of said component at a time subsequent to time n, wherein one of said states of said three state Kalman filter is an estimated filtered health indicator of said first health indicator.

12. The computer program product of claim 11, further comprising machine executable code for:

estimating said first health indicator using a hypothesis determination technique.

13. The computer program product of claim 12, wherein the health of a component at a first time is associated with a third health indicator, and at a second time occurring a predetermined amount of time after the first time, the health of the component is associated with a fourth health indicator, said third indicator and said fourth indicator being classified as varying degrees of a same state of health of the component for said predetermined amount of time.

14. The computer program product of claim 11, wherein said states of said three state Kalman filter include a rate of change of health and acceleration of health condition.

15. The computer program product of claim 14, wherein said rate of change of health is a first derivative of said estimated filtered health indicator and said acceleration is a second derivative of said estimated filtered health indicator.

16. The computer program product of claim 15, further comprising:

determining said second health indicator using at least one of;

said first derivative of said estimated filtered health indicator and said second derivative of said estimated filtered health indicator.

17. The computer program product of claim 11, wherein at least one condition indicator is a normalized condition indicator.

18. The computer program product of claim 11, wherein $H=\left[\begin{array}{ccc}1& 0& 0\end{array}\right]$$\Phi$

=[1dtdt2201dt001]$Q=2<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\sigma <br><br>}}^2<br><br>\stackrel{\_}{t}<br><br>\left[\begin{array}{ccc}\frac{{\mathrm{dt}}^3}{2}& \frac{{\mathrm{dt}}^2}{2}& \stackrel{\_}{t}\\ \frac{{\mathrm{dt}}^2}{2}& \mathrm{dt}& 1\\ \stackrel{\_}{t}& 1& \frac{1}{\stackrel{\_}{t}}\end{array}\right]$$X=\left[\begin{array}{c}\mathrm{HI\_est}\\ \stackrel{.}{H}<br><br>I\\ \stackrel{\mathrm{¨<br><br>}}{H}<br><br>I\end{array}\right]$

in which;

σ

is a power spectral density,

R is a measurement error,

P is a covariance,

Q is a plant noise,

H is a measurement matrix,

K is a Kalman gain,

Φ

is state transition matrix,

{dot over (H)} is a first derivative of HI_est, and

{umlaut over (H)} is a second derivative of HI_est and

19. A computer program product for determining an health indicator of a component at a subsequent time comprising machine executable code for:

determining a first health indicator of said component at a time, n, in accordance with at least one corresponding condition indicator; and

using a three state Kalman filter to determine a second health indicator of said component at a time subsequent to time n; and

estimating said first health indicator using a hypothesis determination technique, wherein said at least one condition indicator is a normalized condition indicator.

20. A computer program product for determining an health indicator of a component at a subsequent time comprising machine executable code for:

determining a first health indicator of said component at a time, n, in accordance with at least one corresponding condition indicator; and

using a three state Kalman filter to determine a second health indicator of said component at a time subsequent to time n, wherein $H=\left[\begin{array}{ccc}1& 0& 0\end{array}\right]$$\mathrm{\Phi <br><br>}=\left[\begin{array}{ccc}1& \mathrm{dt}& \frac{{\mathrm{dt}}^2}{2}\\ 0& 1& \mathrm{dt}\\ 0& 0& 1\end{array}\right]$$Q=2<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\sigma <br><br>}}^2<br><br>\stackrel{\_}{t}<br><br>\left[\begin{array}{ccc}\frac{{\mathrm{dt}}^3}{2}& \frac{{\mathrm{dt}}^2}{2}& \stackrel{\_}{t}\\ \frac{{\mathrm{dt}}^2}{2}& \mathrm{dt}& 1\\ \stackrel{\_}{t}& 1& \frac{1}{\stackrel{\_}{t}}\end{array}\right]$$X=\left[\begin{array}{c}\mathrm{HI\_est}\\ \stackrel{.}{H}<br><br>I\\ \stackrel{\mathrm{¨<br><br>}}{H}<br><br>I\end{array}\right]$

in which;

σ

is a power spectral density,

R is a measurement error,

P is a covariance,

Q is a plant noise,

H is a measurement matrix,

K is a Kalman gain and

Φ

is state transition matrix, and

 

X<sub>t|t−

1</sub>=Φ

X<sub>t−

1|t−

1</sub> 

 

(Equation T1)