A method and system for fault diagnosis of rotating machinery based on multi-scale network structure

Abstract

The invention discloses a rotating machinery fault diagnosis method and system based on a multi-scale network structure. The method includes: collecting the operation data of the rotating machinery, inputting the operation data into the fault diagnosis convolutional neural network, and the fault diagnosis convolutional neural network outputs the diagnosis result; in the fault diagnosis convolutional neural network: The features are fused in the channel dimension, and the fused features are used as the input of the subsequent network; the weight of all or part of the output features of the convolutional layer is obtained through the spatial attention mechanism, and the weight is multiplied by the output feature of the convolutional layer to obtain The scaled features, the scaled features are used as the input of the pooling layer connected after the convolutional layer. The convolutional neural network for fault diagnosis can have high migration ability on different distribution training data and test data, and obtain quite high accuracy and migration on data sets under different rotation speeds and different loads.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a method and system for fault diagnosis of rotating machinery based on a multi-scale network structure. Background technique [0002] With the development of industrial production and science and technology, the structure of modern mechanical equipment is becoming more and more complex, the degree of automation is constantly improving, and the work efficiency is also getting higher and higher. However, once these devices fail, it will affect the operation of the entire system, which may eventually cause huge economic losses and safety consequences. So fault diagnosis plays an important role in modern industry. [0003] Fault diagnosis of rotating machinery is a technology of fault detection, isolation and identification, which can be applied to grasp the information of equipment operation status. There are three basic tasks of fault diagnosis: (1) to judge wh...

AI Technical Summary

Problems solved by technology

Although the existing methods have achieved great success, the above-mentioned methods still have great limitations: the data-driven method is based on the assumption of the same distribution, and the training data and test data are in the same Collected under working conditions or equipment

These significantly degrade the performance of existing methods

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