Method and apparatus for selecting condition indicators in determining the health of a component

• US 20030065482A1
• Filed: 12/04/2001
• Published: 04/03/2003
• Est. Priority Date: 05/24/2001
• 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 6,847,917 B2
• Time in Patent Office: Days
• US Class Current: 702/183
• 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 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06

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 ranking condition indicators used in determining a health indicator for a component comprising:

determining a first set of a plurality of said condition indicators;

determining a covariance matrix corresponding to said plurality of condition indicators;

determining a transformation matrix that whitens the covariance matrix;

using said whitening matrix to determine differences between said first plurality of condition indicators and expected values for said condition indicators belonging to a health class, each health class having a corresponding health indicator; and

selecting a portion of said plurality of condition indicators in accordance with those condition indicators have the smallest of said differences.

2. The method of claim 1, further comprising:

sorting said differences in descending order, each of said differences having a corresponding condition indicator.

3. The method of claim 1, wherein said first plurality of condition indicators correspond to an observed data acquisition.

4. The method of claim 1, further comprising:

determining a measure of between class scatter, Sb, represented as;

$S_b=\underset{i=1}{\overset{L}{\sum <br><br>}}<br><br>P_i<br><br>\left(M_i-M_0\right)<br><br>{\left(M_i-M_0\right)}^T,M_0=\underset{i=1}{\overset{L}{\sum <br><br>}}<br><br>P_i<br><br>M_i.$

where M0 is an expected value of all L classes, Mi represents an expected value of a particular class, and Pi is the probability of a class i.

5. The method of claim 4, further comprising:

determining a whitening transformation of Sb as Sbw represented as;

S_bw=A^TS_bA, for the whitening transformation matrix A.

6. The method of claim 5, wherein said whitening transformation matrix A is:

Λ

<sup>−

1/2</sup>Φ

^T with a corresponding eigenvalue matrix A and a corresponding eigenvector matrix Φ

.

7. A computer program product for ranking condition indicators used in determining a health indicator for a component comprising machine executable code for:

determining a first set of a plurality of said condition indicators;

determining a covariance matrix corresponding to said plurality of condition indicators;

determining a transformation matrix that whitens the covariance matrix;

using said whitening matrix to determine differences between said first plurality of condition indicators and expected values for said condition indicators belonging to a health class, each health class having a corresponding health indicator; and

selecting a portion of said plurality of condition indicators in accordance with those condition indicators have the smallest of said differences.

8. The computer program product of claim 7, further comprising machine executable code for:

sorting said differences in descending order, each of said differences having. a corresponding condition indicator.

9. The computer program product of claim 7, wherein said first plurality of condition indicators correspond to an observed data acquisition.

10. The computer program product of claim 7, further comprising machine executable code for:

determining a measure of between class scatter, Sb, represented as;

$S_b=\underset{i=1}{\overset{L}{\sum <br><br>}}<br><br>P_i<br><br>\left(M_i-M_0\right)<br><br>{\left(M_i-M_0\right)}^T,M_0=\underset{i=1}{\overset{L}{\sum <br><br>}}<br><br>P_i<br><br>M_i.$

where M0 is an expected value of all L classes, Mi represents an expected value of a particular class, and Pi is the probability of a class i.

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

determining a whitening transformation of Sb as Sbw represented as;

S_bw=A^TS_bA, for the whitening transformation matrix A.

12. The computer program product of claim 11, wherein said whitening transformation matrix A is:

Λ

<sup>−

1/2</sup>Φ

^T with a corresponding eigenvalue matrix Λ and

a corresponding eigenvector matrix Φ

.