Local migration diagnosis method for rolling bearing fault depth based on domain asymmetry factor weighting

Abstract

The deep local migration diagnosis method of rolling bearing faults weighted by domain asymmetry factors firstly uses the deep residual network to extract the deep migration fault features in the monitoring data of the source rolling bearing and the target rolling bearing; secondly uses the deep migration fault features to train the domain confusion network and calculates the domain difference Symmetric factor; calculate the multi-core maximum mean difference of the deep residual network adaptation layer fault features again, and use the domain asymmetry factor to weight and suppress the contribution of invalid fault features in the source rolling bearing; finally use the weighted multi-core maximum mean difference to construct the objective function, Training deep residual network; the trained local migration diagnosis model composed of domain confusion network and deep residual network can effectively overcome the adverse effects of domain asymmetry factors on migration diagnosis, and realize the identification of target rolling bearings by using local diagnostic knowledge of source rolling bearings. Unbalanced health states, significantly improving the diagnostic accuracy of migration diagnostic models.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing fault diagnosis, and in particular relates to a rolling bearing fault depth local migration diagnosis method weighted by a domain asymmetry factor. Background technique [0002] Rolling bearings are one of the core components of large-scale rotating machinery. Once a failure occurs, it will cause huge economic losses, and even endanger the lives of personnel. Therefore, its healthy service is very important. Fault intelligent diagnosis uses advanced machine learning technology to construct the mapping relationship between bearing monitoring data and health status, which greatly reduces the excessive reliance on expert prior knowledge in the diagnosis process, especially with the vigorous development of deep learning technology in recent years, The intelligent level and diagnostic accuracy of fault intelligent diagnosis have been significantly improved, and it has become an important means ...

AI Technical Summary

Problems solved by technology

[0003] The existing rolling bearing migration diagnosis technology has significant limitations: the diagnostic knowledge domains of the source bearing and the target bearing are symmetrical, that is, it is required that ① the data of the target bearing must be evenly distributed in each health state; The size is equal to the tag space size of the target bearing data

However, in engineering practice, these two points are often untenable: the target bearing is in a normal state for a long time during service, and the frequency of fault states is significantly less than that of the normal state. In addition, the fault state generated by the source bearing may not occur on the target bearing. , therefore, the data distribution of the target bearing is severely unbalanced (contains a large amount of normal information and a small amount of fault information), and the label space of the source rolling bearing data often covers the label space of the target bearing, finally forming an asymmetric source bearing and target bearing diagnosis knowledge area

[0004] Affected by the asymmetric factors of the diagnostic knowledge domain, it is difficult for existing migration diagnostic techniques to effectively use the diagnostic knowledge of the source bearing to identify the unbalanced health status of the target bearing

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