Rotary machine fault diagnosis method based on improved convolutional neural network

A technology of convolutional neural network and rotating machinery, applied in biological neural network models, neural architecture, geometric CAD, etc., can solve the problems of easy error deletion of small fault features, large subjectivity and blindness, noise masking, etc., to reduce training Flexible and convenient monitoring of parameters, calculation time, and operating status, and the effect of improving diagnostic speed and efficiency
CN112541233AInactive Publication Date: 2021-03-23宫文峰
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
宫文峰
Publication Date
2021-03-23
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a rotary machine fault diagnosis method based on an improved convolutional neural network, and the method comprises the following steps: (1) collecting one-dimensional time sequence fault data, and obtaining an original fault data set; (2) performing preprocessing operation on the acquired data of the original fault data set, wherein the preprocessing comprises standardization, data truncation and data reconstruction; (3) dividing the preprocessed samples of each type of faults into a training set, a verification set and a test set; (4) an improved convolutional neuralnetwork fault diagnosis model is established, the model comprises an input layer, a feature extraction layer, a dimension reduction and parameter reduction layer and a softmax classification output layer, and the dimension reduction and parameter reduction layer comprises a 1 * 1 transition convolution layer and a global mean pooling layer; (5) training and testing the model; wherein the diagnosismodel can automatically extract features and diagnose the fault data without any manual feature extraction operation, so that people can diagnose the fault of the rotating machine more conveniently and quickly.
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Description

technical field

[0001] The invention belongs to the technical field of fault diagnosis and detection of rotating machinery, and more specifically relates to a fault diagnosis method for rotating machinery based on an improved convolutional neural network. Background technique

[0002] With the rapid development of modern industrial technology, rotating machinery equipment is increasingly developing towards high speed, precision, automation and integration. Rotating machinery mainly includes power devices, such as diesel engines, steam turbines, engines, motors, etc., and also includes rotating parts , such as bearings, spindles, etc. With the diversification of the working environment of rotating machinery, especially when it operates continuously for a long time in a complex and changeable working environment, it is often prone to various failures due to its heavy workload, variable load, and the influence of salt-alkali corrosion and high temperature. . If the fault cann...

Claims

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