Bearing fault diagnosis method under variable working condition based on mixed entropy and joint distribution adaptation

Abstract

The invention discloses a bearing fault diagnosis method under a variable working condition based on mixed entropy and joint distribution adaptation. An original sample data set of rolling bearing vibration is divided according to whether the load condition is known to establish a source domain data set and a target domain data set; time-frequency decomposition is carried out on different sample signals in the source domain data set and the target domain data set to obtain an intrinsic mode function of each sample signal; a nonlinear entropy parameter of each IMF component of each sample signal is calculated according to a nonlinear metric entropy theory, and a multi-scale mixed entropy feature vector of the sample signal is constructed; and according to transfer learning theory, the multi-scale mixed entropy feature vectors of all sample signals in the source domain data set and the target domain data set are used for constructing a rolling bearing fault diagnosis model under the variable working condition based on joint distribution adaptation, and a final diagnosis result is output. The method can effectively solve the problem in recognizing various fault states of the bearing under the unknown load condition, remarkably improves the fault diagnosis precision, and improves the stable operation level of the bearing.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis, and in particular relates to a fault diagnosis method under variable working conditions. Background technique [0002] Rolling bearings are one of the most common basic components in modern mechanical equipment. By converting the sliding friction between the running shaft and the shaft seat into rolling friction, the friction loss is reduced and the operating efficiency of the mechanical equipment is effectively improved. Studies have shown that abnormal operation or accidents of mechanical equipment caused by rolling bearing failures account for a large proportion of equipment failure cases, and rolling bearing failures can usually be reflected in the monitored vibration signals. Therefore, based on the collected vibration signal samples of rolling bearings, research and design of efficient and feasible rolling bearing fault diagnosis methods can effectively improve the accuracy of beari...

AI Technical Summary

Problems solved by technology

Aiming at the fault diagnosis of rolling bearings under variable working conditions, there has not been systematic and in-depth research on the fault diagnosis of rolling bearings under variable working conditions. There is a lack of reasonable and complete bearing fault diagnosis methods based on mixed entropy and joint distribution adaptation. Functional requirements for maintenance

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