Deep transfer learning intelligent fault diagnosis method and device, storage medium and equipment

Abstract

The invention provides a deep transfer learning intelligent fault diagnosis method and device, a storage medium and equipment. The method comprises the steps of obtaining to-be-identified target domain data; inputting the target domain data into a fault diagnosis migration convolutional neural network model for fault judgment to obtain a health condition label; the training method of the fault diagnosis migration convolutional neural network model comprises the following steps: respectively inputting a source domain sample and a target domain sample into the fault diagnosis migration convolutional neural network model to extract features; minimizing a health state classification error on the source domain sample through a cross entropy loss function; calculating a covariance distance of the features between the source domain sample and the target domain sample through a domain adaptive module; and constraining the parameters by iteratively optimizing a loss function. According to the invention, the data distribution difference between the source domain and the target domain can be reduced, the purpose of predicting the target domain label is achieved, and the migration accuracy ofthe health condition label from the source domain to the target domain is effectively improved.

Description

technical field [0001] The present invention relates to the technical field of fault data processing, and more specifically, to a deep transfer learning intelligent fault diagnosis method, device, storage medium and equipment. Background technique [0002] Mechanical fault diagnosis is of great significance to ensure the safe operation of equipment, because once an accident occurs in mechanical equipment, it will bring huge economic losses and casualties. In recent years, due to the rapid development of sensor technology and computing power, fault diagnosis has gradually received attention in industry and academia. In the research of fault diagnosis based on signal processing, the traditional method of feature extraction mode and machine learning classification mode has achieved good results; for example, using K nearest neighbor classification algorithm, support vector machine algorithm and BP neural network method, etc. However, the above methods all use the features extr...

AI Technical Summary

Problems solved by technology

However, the limitation of using MMD in the domain adaptation layer is that when calculating the integral probability measure (IPM), as the number of samples increases, the computational cost of MMD will increase exponentially

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