Method and apparatus for diagnosing a mechanism

• US 7,430,483 B2
• Filed: 09/27/2006
• Issued: 09/30/2008
• Est. Priority Date: 05/05/2006
• Status: Active Grant

• Current Assignee: Airbus Helicopters
• Original Assignee: Eurocopter
• Inventors: Wiig, Johan
• Primary Examiner(s): Lau; Tung S
• Assistant Examiner(s): Kundu; Sujoy K
• Application Number:
  US11/527,403
• Publication Number:
  US 20070260656A1
• Time in Patent Office: 734 Days
• Field of Search: 702/69, 702/34, 702/56, 701100-111, 735/79, 736/59, 736/60
• US Class Current: 702/69
• 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: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods

Abstract

A method of analyzing variations in an indicator of the behavior of a mechanism, in which noise is extracted, and any discontinuities from an indicator in order to obtain a trend, variations in the trend are compared with one or more reference values; and a fault of the mechanism is indicated as a function of the result of the comparison. Any outlier data in the indicated data are search for and subtracted, and any edge data are extracted from the data obtained from the search operation.

Claims

1. A method of analyzing variations in an indicator of the behavior of a mechanism, the method being characterized in that it comprises the following operations:

extracting noise and any discontinuities from the indicator in order to obtain a trend;

comparing variations in the trend with one or more reference values;

indicating a fault of the mechanism as a function of the result of the comparison;

wherein, in order to extract any discontinuities, the following operations are also performed;

searching for and subtracting out any outlier data in the indicator data; and

extracting any edge data from the data obtained from said searching operation.

2. A method according to claim 1, in which, in order to find the times of the outlier data:

a moving median value of the indicator data is subtracted from the indicator data in order to obtain noisy data; and

the time is identified for which the ratio of the amplitude of the noisy data to the indicator data level is greater than a threshold.

3. A method according to claim 1, in which, in order to identify the presence of an edge in the data, variations in the scale coefficients corresponding to a wavelet transform of variations in the data are analyzed.

4. A method according to claim 3, in which a potential edge is identified in the data when the ratio b_p of a mean of the sale coefficients to the standard deviation of the variations of these coefficients, exceeds a first determined value t_p, and when the ratio b_m of said mean of the scale coefficients to the level of the data exceeds a second determined value t_m.

5. A method according to claim 1, in which, the following operations are also performed:

calculating the level of the noise;

comparing variations in the level of the noise with one or more reference values; and

indicating a fault of the mechanism as a function of the results of the comparison performed in operation.

6. A method according to claim 5, in which, in order to compare variations in the noise level with reference values, the following operations are performed:

calculating scale variation data of a wavelet transform of the noise level variations; and

comparing the scale variation data of the wavelet transform of the noise level with the reference values.

7. A method according to claim 3, in which use is made of the Haar wavelet and of the scale function associated with the Haar wavelet.

8. A method according to claim 1, in which, when an edge is detected, the time of the edge is compared with historical data concerning maintenance of the mechanism.

9. A method according to claim 1, in which the amplitude of the data is compared with at least one third reference value.

10. A method according to claim 4, in which a fault of the mechanism is diagnosed as a function of the results of the comparisons.

11. A method according to claim 1, further including the following successive prior operations:

normalizing variations of the indicator with reference to simultaneous variations of an operating parameter of the mechanism; and

then, in a training stage, determining reference values or thresholds for the rate of variation of at least one component of the indicator from the variations of the indicator during a period of operation without any fault of the mechanism.

12. A method according to claim 11, in which the normalization operation includes parametric modeling of the indicator or of measurements from the indicator is extracted.

13. A method according to claim 11, in which the normalization operation comprises filtering indication variation data.

14. A method according to claim 1, in which the mechanism is a rotorcraft transmission mechanism, the indicator is derived from vibration measurements performed on the rotorcraft in flight, and the operating parameter is the air speed of the rotorcraft.

15. A method according to claim 1, in which, in order to compare the trend variations with reference values, the following operations are performed:

calculating scale variation data of a wavelet transform of the trend data; and

comparing the scale variation data of the wavelet transform of the trend data with reference values.

16. Apparatus for diagnosing a mechanism as a function of data generated from vibration measurements performed on the mechanism, the apparatus being characterized in that it is programmed to perform the operations of a method comprising the following operations:

extracting noise and any discontinuities from the indicator in order to obtain a trend;

comparing variations in the trend with one or more reference values;

indicating a fault of the mechanism as a function of the result of the comparison;

wherein, in order to extract any discontinuities, the following operations are also performed;

searching for and subtracting out any outlier data in the indicator data; and

extracting any edge data from the data obtained from said searching operation.