Trending of vibration data taking into account torque effect

• US 8,355,879 B2
• Filed: 01/06/2010
• Issued: 01/15/2013
• Est. Priority Date: 01/06/2010
• 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: Bechhoefer, Eric Robert
• Primary Examiner(s): KUNDU, SUJOY K
• Application Number:
  US12/655,720
• Publication Number:
  US 20110166799A1
• Time in Patent Office: 1,105 Days
• Field of Search: 702 33- 35, 73/10.9
• US Class Current: 702/35
• CPC Class Codes: G01H 1/003 of rotating machines G01H1/10 takes precedence
• CPC Class Codes: G01M 13/028 Acoustic or vibration analysis
• CPC Class Codes: G01M 7/00 Vibration-testing of structures; Shock-testing of structures G01M9/00 takes precedence
• CPC Class Codes: G01N 2291/0258 Structural degradation, e.g. fatigue of composites, ageing of oils
• CPC Class Codes: G01N 2291/02827 Elastic parameters, strength or force
• CPC Class Codes: G01N 2291/2693 Rotor or turbine parts
• CPC Class Codes: G01N 29/045 by imparting shocks to the workpiece and detecting the vibrations or the acoustic waves caused by the shocks measuring resonant frequency G01H13/00; measuring strength properties by application of mechanical stress G01N3/00
• CPC Class Codes: G01N 29/14 using acoustic emission techniques echo of particles G01N29/046; measuring mechanical vibrations or acoustic waves in solids in general G01H1/00
• CPC Class Codes: G01N 29/4463 Signal correction, e.g. distance amplitude correction [DAC], distance gain size [DGS], noise filtering
• CPC Class Codes: G01N 29/449 Statistical methods not provided for in G01N29/4409, e.g. averaging, smoothing and interpolation
• CPC Class Codes: G01N 29/50 using auto-correlation techniques or cross-correlation techniques

Abstract

In vibration based mechanical diagnostics, algorithms may extract some feature of a component which may be used as a statistic of the components'"'"' health. A measured condition indicator corresponding to the analysis of the components'"'"' health may be a function of transmission error (TE). Because there exists a relationship between TE and the CI for a component that is faulted, the CI is correlated with power transmitted. In drive train components, power may be proportional to measured torque. In terms of diagnostics or trending of a component, it is desirable to reduce the scatter in the measured CI and account for this torque. The relationship between measured CI and torque may be captured over time. An increase in the correlation of torque with the CI would indicate an increase of TE which indicates the propagation of a fault.

Claims

1. A method of indicating a propagating fault in a component, comprising:

measuring data corresponding to a condition indicator of the component and to an input factor, wherein the condition indicator of the component includes a vibration measure and the input factor is torque;

determining, using at least one processor, a relationship between the condition indicator and the input factor;

determining, using at least one processor, an effect of the input factor on the condition indicator;

determining, using at least one processor, a filtered condition indicator based on the effect of the input factor on the condition indicator; and

determining, using at least one processor, whether a propagating fault is indicated based on a correlation over a time period of the filtered condition indicator with the input factor, wherein the propagating fault is indicated according to the correlation over the time period of the filtered condition indicator with the torque input factor where the effect of the torque input factor on the condition indicator is determined as statistically significant in connection with trending of the filtered condition indicator over the time period.

2. The method according to claim 1, wherein determining the effect of the input factor on the condition indicator includes using a state observer.

3. The method according to claim 2, wherein the state observer is at least one of:

a Kalman filter, a recursive least squares estimation, and a Particle filter.

4. The method according to claim 1, wherein the component is a rotating shaft.

5. The method according to claim 1, wherein data of the filtered condition indicator has a reduced variance compared to the data measured corresponding to the condition indicator.

6. The method according to claim 1, wherein the propagating fault is determined based on a positive correlation over time between the filtered condition indicator and the input factor.

7. A non-transitory computer readable storage medium storing computer software that determines indication of a propagating fault in a component, the computer software comprising:

executable code that measures data corresponding to a condition indicator of the component and to an input factor, wherein the condition indicator of the component includes a vibration measure and the input factor is torque;

executable code that determines a relationship between the condition indicator and the input factor;

executable code that determines an effect of the input factor on the condition indicator;

executable code that determines a filtered condition indicator based on the effect of the input factor on the condition indicator; and

executable code that determines whether a propagating fault is indicated based on a correlation over time of the filtered condition indicator with the input factor, wherein the propagating fault is indicated according to the correlation over the time period of the filtered condition indicator with the torque input factor where the effect of the torque input factor on the condition indicator is determined as statistically significant in connection with trending of the filtered condition indicator over the time period.

8. The non-transitory computer readable storage medium according to claim 7, wherein the executable code that determines the effect of the input factor includes executable code that uses a state observer.

9. The non-transitory computer readable storage medium according to claim 8, wherein the state observer includes at least one of:

a Kalman filter, a recursive least squares estimation, and a Particle filter.

10. The non-transitory computer readable storage medium according to claim 7, wherein the component is a rotating shaft.

11. The non-transitory computer readable storage medium according to claim 7, wherein data of the filtered condition indicator has a reduced variance compared to the data measured corresponding to the condition indicator.

12. The non-transitory computer readable storage medium according to claim 7, wherein the propagating fault is determined based on a positive correlation over time between the filtered condition indicator and the input factor.

13. A system for indicating a propagating fault in a component, comprising:

a first sensor that measures data corresponding to a condition indicator of the component, wherein the condition indicator of the component includes a vibration measure; and

a second sensor that measures data corresponding to an input factor, wherein the input factor is torque; and

at least one processor that processes the data measured from the first sensor and the second sensor, the at least one processor executing computer software stored on a computer readable storage medium, the computer software comprising;

executable code that determines a relationship between the condition indicator and the input factor;

executable code that determines an effect of the input factor on the condition indicator;

executable code that determines a filtered condition indicator based on the effect of the input factor on the condition indicator; and

executable code that determines whether a propagating fault is indicated based on a correlation over time of the filtered condition indicator with the input factor, wherein the propagating fault is indicated according to the correlation over the time period of the filtered condition indicator with the torque input factor where the effect of the torque input factor on the condition indicator is determined as statistically significant in connection with trending of the filtered condition indicator over the time period.

14. The system according to claim 13, wherein the executable code that determines the effect of the input factor includes executable code that uses a state observer.

15. The system according to claim 14, wherein the state observer includes at least one of:

a Kalman filter, a recursive least squares estimation, and a Particle filter.

16. The system according to claim 13, wherein data of the filtered condition indicator has a reduced variance compared to the data measured corresponding to the condition indicator.

17. The system according to claim 13, wherein the propagating fault is determined based on a positive correlation over time between the filtered condition indicator and the input factor.