Mechanical fault diagnosis method and system based on TJM transfer learning

Abstract

The invention discloses a mechanical fault diagnosis method and system based on TJM transfer learning. According to the method, CEEMDAN decomposition is introduced. The algorithm calculation amount isreduced while the mode mixing problem is solved, and meanwhile the problems that according to a traditional machine learning method, when training and testing data distribution has a certain degree of difference, the established classification model is poor in popularization capacity, and even sometimes the classification model cannot be universally used are solved through a transfer learning method. And meanwhile, the problem of low fault diagnosis efficiency caused by data difference between different working conditions of the rotary machine is solved, and the problems that the fault stateis incomplete and the fault diagnosis cannot be correctly and completely carried out due to insufficient data acquisition quantity of the rotary machine in some working states are also solved. According to the method, the characteristic that cross-domain feature matching and instance reweighting are jointly executed in the TJM transfer learning method is utilized, the problem that the recognitionand diagnosis rate is not high due to the fact that the data difference between the source domain and the target domain is large is solved to the maximum degree, and the fault diagnosis precision is greatly improved.

Description

technical field [0001] The invention relates to the technical field of intelligent diagnosis of mechanical faults, in particular to a method and system for diagnosing mechanical faults based on TJM transfer learning. Background technique [0002] The timely and accurate detection and fault diagnosis of rolling bearings are crucial to ensure the reliability of rotating machinery, so effective fault diagnosis is conducive to timely and accurate prevention of equipment failures. However, in the actual engineering application of rotating machinery, the operating status data of some machines is not complete enough, and the working conditions are often changing. In recent years, more and more attention has been paid to the research on intelligent fault diagnosis of rotating machinery under such unknown working conditions. Unfortunately, bearing data for incomplete operating conditions and unknown operating conditions for rotating machinery are often very scarce. Furthermore, it ...

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Problems solved by technology

[0003] The purpose of the present invention is to provide a mechanical fault diagnosis method and system based on TJM transfer lea

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