Bearing fault classification method and system based on deep learning network

Abstract

The invention provides a bearing fault classification method and system based on a deep learning network, and the method comprises the steps: setting a sampling frequency, and collecting the vibrationsignal data of a bearing under different working conditions; segmenting the obtained vibration signal data to construct a plurality of samples; decomposing the vibration signal data of each sample toobtain a plurality of modal components so as to realize effective component separation; constructing a deep network with a residual error unit, and determining an appropriate network depth by using arandom search method; inputting the training set into a deep residual network for iterative training and obtaining a classification model; and inputting the test set into the classification model toobtain a fault classification result. According to the classification method provided by the invention, variational mode decomposition and a deep residual network are combined; the problems that noiseinterference exists in input data, cross aliasing exists in effective components, network deepening causes identification gradient disappearance, and performance degradation causes poor classification effect are solved, fault feature extraction not affected by rotating speed changes is achieved, and the fault classification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of bearing fault diagnosis, and relates to a bearing fault classification method and system based on a deep learning network. Background technique [0002] Once the shaft diameter bearing of the running part of the high-speed train breaks down during operation, it will directly lead to car crash and death, and the consequences will be disastrous. Therefore, it is necessary to monitor the state of the shaft and diameter bearings of the running part, and carry out early fault feature extraction and classification judgment in a timely manner. Relevant statistical data show that about 30% of rotating machinery failures are caused by damage to rolling bearings; rolling bearing failures in induction motor failures account for about 40% of motor failures, and bearing failure rates in various types of gearbox failures are second only to gears. 20%. Therefore, the state monitoring of the shaft diameter bearing of t...

AI Technical Summary

Problems solved by technology

At the same time, it solves the problems of deep residual network input data noise reduction and effective component anti-aliasing, gradient disappearance caused by network deepening, and poor classification effect caused by performance degradation in the existing technology.

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