Motor bearing fault diagnosis method based on feature optimization and gwoa-xgboost

Abstract

The invention relates to a motor bearing fault diagnosis method based on feature optimization and GWOA-XGBoost, comprising the following steps: using optimized VMD to decompose the vibration signal of the motor bearing to obtain its IMF component, and calculating the IMF component based on the multi-scale entropy theory MSE value, reconstruct the characteristics of the vibration signal through the MSE value; use the GA algorithm to improve the WOA algorithm to obtain the GWOA model, optimize the XGBoost model through the GWOA model; input the characteristic reconstructed vibration signal into the optimized XGBoost model , to obtain the motor bearing fault diagnosis results. The motor bearing fault diagnosis method of the invention can improve the accuracy of the motor bearing fault diagnosis.

Description

technical field [0001] The invention relates to the technical field of motor fault diagnosis, in particular to a motor bearing fault diagnosis method based on feature optimization and GWOA-XGBoost. Background technique [0002] Bearings play a role in supporting and guiding the rotation of the motor. According to statistics, about 40% of motor failures are caused by bearing failures. They are an important object of motor health monitoring and directly affect the overall performance of motor operation. Therefore, it is of great significance to carry out research on fault diagnosis of motor bearings. Because the motor bearing is located inside the motor, its fault state is difficult to identify, which limits the accuracy of its fault diagnosis. [0003] At present, bearing fault diagnosis based on vibration signal is the most common method. When the motor bearing fails, the vibration signal will have obvious shock fluctuations. If the time-frequency signal of the vibration si...

AI Technical Summary

Problems solved by technology

[0011] Aiming at the technical problems existing in the prior art, the present invention provides a motor bearing fault diagnosis method based on feature optimization and GWOA-XGBoost to solve the problem of improving the accuracy of motor bearing fault diagnosis

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