Gyroscope fault diagnosis method based on K-S (Kolmogorov-Smirnov) distribution check and HHT (Hilbert-Huang Transform)

A technology of fault diagnosis and inspection method, which is applied in the direction of instruments, special data processing applications, measuring devices, etc., can solve the problem of low accuracy of fault diagnosis, achieve good fault diagnosis and simplify the detection process

Inactive Publication Date: 2014-09-17
HARBIN INST OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The present invention aims to solve the problems existing in the existing gyroscope fault diagnosis methods such as false frequency components and low accuracy of fault diagnosis, and provides a gyroscope fault diagnosis method based on K-S distribution test and HHT

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  • Gyroscope fault diagnosis method based on K-S (Kolmogorov-Smirnov) distribution check and HHT (Hilbert-Huang Transform)
  • Gyroscope fault diagnosis method based on K-S (Kolmogorov-Smirnov) distribution check and HHT (Hilbert-Huang Transform)
  • Gyroscope fault diagnosis method based on K-S (Kolmogorov-Smirnov) distribution check and HHT (Hilbert-Huang Transform)

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specific Embodiment approach 1

[0028] Specific embodiment one: the gyroscope fault diagnosis method based on K-S distribution test and HHT of the present embodiment is realized according to the following steps:

[0029] Step 1: Decompose the original gyro angular velocity output signal Xp using the EMD method to obtain IMF components in different frequency bands;

[0030] Step 2: Utilize the K-S distribution test method to carry out correlation test to the IMF component of different frequency bands obtained in step 1, judge whether the IMF component of different frequency bands is the effective component of original gyroscope angular velocity output signal;

[0031] Step 3: Perform HHT transformation on the IMF component tested by the K-S method in step 2, and then obtain the time spectrum and marginal spectrum of the IMF component, and combine the energy and frequency changes of the signal on the time spectrum and the signal frequency distribution judgment system on the marginal spectrum Whether there is a...

specific Embodiment approach 2

[0037] Specific embodiment two: the difference between this embodiment and specific embodiment one is: the original gyro angular velocity output signal Xp is decomposed by the EMD method in the step one, and the different frequency band IMF components obtained are specifically:

[0038] (1) Use the matlab extremum function to find out all local extremum points on the time series of the original gyro angular velocity output signal Xp;

[0039] (2) The upper envelope function and the lower envelope function of Xp are generated by constructing the maximum value and the minimum value respectively, denoted as e max (t) and e min (t);

[0040] (3) Generate the mean function of the upper and lower envelope functions of Xp: get the mean function m of the upper and lower envelope functions of Xp based on formula (1) 1 (t);

[0041] m 1 ( t ) = e ...

specific Embodiment approach 3

[0058] Specific embodiment three: what this embodiment is different from specific embodiment one or two is: the IMF component of the different frequency bands that obtains in described step 2 utilizes the K-S distribution test method to carry out correlation test, and judges whether the IMF component of different frequency bands is The effective components of the raw gyro angular velocity output signal:

[0059] (1) K-S distribution test:

[0060] For N-point time series y(n)={y 1 ,y 2 ,...,y n}, define its cumulative distribution function:

[0061] E i = n ( i ) N - - - ( 6 )

[0062] Among them, n(i) is the number of samples whose data value is less than y(i) in the sample population obtained by first sorting the data samples ...

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Abstract

The invention provides a gyroscope fault diagnosis method based on K-S (Kolmogorov-Smirnov) distribution check and HHT (Hilbert-Huang Transform), relates to a fault diagnosis method of a gyroscope, and mainly solves the problems of an existing gyroscope fault diagnosis method that a virtual frequency component is generated and the fault diagnosis precision is low. The gyroscope fault diagnosis method comprises the following steps: step 1: decomposing an original gyroscopic angle speed output signal Xp by adopting an EMD (Empirical Mode Decomposition) method to obtain IMF (Intrinsic Mode Function) components of different frequency bands; step 2: carrying out a correlation test on the IMF components of the different frequency bands in the step 1 by using a K-S distribution check method and judging that whether the IMF components of the different frequency bands are effective components of the original gyroscopic angle speed output signal or not; and step 3: carrying out the HHF on the IMF components checked by the K-S method in the step 2 to further obtain time-frequency spectrums and marginal spectrums of the IMF components, and combining with energy and frequency change of signals on the time-frequency spectrums and signal frequency distribution on the marginal spectrums to judge whether the system has faults in an operation process or not. The gyroscope fault diagnosis method is applied to the field of signal processing.

Description

technical field [0001] The present invention relates to a fault diagnosis method of a gyroscope, in particular to a fault diagnosis method based on Kolmogorov-Smirnov ("K-S" for short) distribution test and Hilbert-Huang Transform (HHT). Background technique [0002] In recent years, signal processing technology has been continuously developed, and due to the continuous progress and perfection of signal processing disciplines, signal processing methods have also been continuously improved and updated. Algorithms such as short-time Fourier transform and wavelet transform transform the object signals that can be processed from the previous stationary signals to non-stationary signals, but since these methods are based on Fourier transform, when facing non-stationary signals , they exhibit some of the drawbacks associated with the Fourier transform, such as the generation of spurious frequency components. The Hilbert-Huang Transform (HHT) performs very well in dealing with non...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C25/00G06F19/00
CPCG01C25/00
Inventor 王敏金晶沈毅崔捷刘攀
Owner HARBIN INST OF TECH
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