Mechanical rubbing fault diagnosis method based on multi-wavelet kernel-support vector regression (SVR)

A technology of support vector regression and fault diagnosis, which is applied in the testing of mechanical parts, the testing of machine/structural parts, and measuring devices, etc. It can solve problems such as inability to know, large amount of calculation, difficulty, etc., and achieve good signal noise reduction effect, The effect of high prediction accuracy and high signal-to-noise ratio
CN109839265AInactive Publication Date: 2019-06-04XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
Publication Date
2019-06-04
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to a mechanical rubbing fault feature extraction method based on multi-wavelet kernel-support vector regression (SVR). The method comprises the following steps: 1) a vibration signal of a mechanical key component is measured and taken, and the signal is subjected to redundant lifting scheme wavelet (RLSW) decomposition; 2) each layer of detail signals and the last layer of approximation signals obtained by decomposition are subjected to adaptive singular value decomposition (ASVD) noise reduction; 3) a signal is reconstructed by redundant lifting wavelets, and a noise reduction signal is acquired; 4) with two interval three-time Hermite spline multi-wavelet scale functions as interpolation basis functions, a novel multi-wavelet kernel function is constructed; and 5) the vibration signals after noise reduction are divided into a training set and a prediction set, the training set is used to construct a prediction model, the prediction set is predicted, and a weak periodic impact component hidden in the original vibration signal is extracted by the prediction residual. Thus, the mechanical rubbing fault features can be extracted.
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Description

technical field

[0001] The invention belongs to the technical field of mechanical fault diagnosis, and relates to a method for extracting mechanical fault features, in particular to a mechanical fault diagnosis method based on a multi-wavelet kernel-support vector regression machine. Background technique

[0002] By analyzing vibration signals, mechanical faults can be diagnosed without stopping the machine. The traditional method is to judge mechanical faults through the time domain and frequency domain indicators of mechanical vibration signals. Time-domain indicators include average value, effective value, peak value, peak-to-peak value, pulse index, margin index, skewness index and kurtosis index, etc. Frequency domain indicators include spectral center of gravity, mean square frequency, and frequency domain variance. These indicators can be used to roughly judge the mechanical state, but it is difficult to diagnose the specific fault type. Some rotating machinery fau...

Claims

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