Intelligent fault diagnosis method under small sample based on attention mechanism element learning model

Abstract

The invention discloses an intelligent fault diagnosis method under a small sample based on an attention mechanism element learning model. According to the intelligent fault diagnosis method, an attention mechanism and a meta-learning method are used for establishing an association network model; short-time Fourier transform is carried out on mechanical signals to obtain a time-frequency spectrogram of the mechanical signals; feature extraction and operation state recognition are further carried out on the time-frequency spectrogram; and rich fault information contained in the mechanical signals can be effectively mined. According to the intelligent fault diagnosis method, a pseudo distance can be trained adaptively to evaluate the similarity between related data; clear mathematical formula definition is not needed; and high mechanical equipment fault diagnosis accuracy can be obtained. Therefore, the dependence of a feature extraction process on artificial experience and the dependence of an existing intelligent fault diagnosis algorithm on a large amount of training data in a traditional diagnosis method are eliminated, and the problem of mechanical equipment fault diagnosis under the condition of small sample data is practically solved.

Description

technical field [0001] The invention relates to the field of fault diagnosis of mechanical equipment, in particular to an intelligent fault diagnosis method based on a meta-learning model of an attention mechanism under small samples. Background technique [0002] Due to the rapid development of computer science and communication technology, a large amount of industrial field data has been recorded and preserved. However, the amount of data containing rich fault information is very small, and most of them are useless data. On the other hand, the artificial fault data collected in the laboratory is difficult to completely simulate the occurrence of real faults. At the same time, it is even more difficult to obtain data on the natural development of mechanical equipment faults, which requires a lot of manpower and material resources. The above problems contradict the preconditions of the existing intelligent fault diagnosis algorithms, because the existing intelligent fault d...

AI Technical Summary

Problems solved by technology

It can be seen that the small sample problem has seriously affected the timeliness, effectiveness and versatility of intelligent fault diagnosis algorithms for fault diagnosis and condition monitoring of mechanical equipment. Research on Fault Diagnosis Method of Mechanical Equipment under Low Level Problems

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