Motor bearing fault diagnosis method based on generalized S transformation and WOA-SVM

Abstract

The invention relates to a motor bearing fault diagnosis method based on generalized S transformation and WOA-SVM, and the method comprises the steps: inputting a motor bearing vibration signal, and obtaining two time-frequency matrixes after two times of different generalized S transformation; respectively obtaining a time domain cumulative characteristic curve with high time resolution and a frequency domain cumulative characteristic curve with high frequency resolution; obtaining a time domain feature and a frequency domain feature of the original signal; combining the time domain featuresand the frequency domain features to form a feature vector sample set, and dividing the feature vector sample set into training samples and test samples; inputting the training sample into a support vector machine optimized by a whale optimization algorithm WOA, and training a classifier; and inputting the test sample into the trained classifier WOA-SVM for testing, and outputting a fault diagnosis type. The method overcomes the defect that the Gaussian window function of S transformation cannot be adjusted along with the frequency and lacks flexibility, has better time-frequency analysis capability, and is more suitable for processing complex non-stationary and non-linear bearing vibration signals.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a motor bearing fault diagnosis method based on generalized S transformation and WOA-SVM. Background technique [0002] Rolling bearings are called "industrial joints" and are widely used in various electrical equipment. When the motor is running, wear, overload, corrosion and other reasons may cause local damage to the motor bearing. Due to the complexity of the rotating machinery transmission system and the diversity of working conditions, the bearing vibration signal has non-stationary and nonlinear characteristics. Therefore, the key to accurate fault diagnosis of motor bearings is to extract effective fault features from the bearing vibration signal. [0003] Time-frequency analysis is to map the one-dimensional time-domain signal and frequency-domain signal to the two-dimensional time-frequency plane to obtain the time-frequency distribution of the signal, which is...

AI Technical Summary

Problems solved by technology

However, the classification performance of SVM is limited by the setting of kernel function parameters and its own structural parameters. How to select appropriate parameters has always been a key problem to be solved in the application of SVM.

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