Convolutional neural network fault diagnosis method based on multi-channel attention module

Abstract

The invention discloses a convolutional neural network fault diagnosis method based on a multi-channel attention module. The method comprises the following steps of: 1, collecting vibration signals ofto-be-diagnosed equipment under different working conditions; 2, performing data enhancement processing and fault labeling on the vibration signals acquired in the step 1; 3, establishing a convolutional neural network fault diagnosis model based on a multi-channel attention module; 4, inputting a vibration signal under a certain working condition into the convolutional neural network fault diagnosis model based on a multi-channel attention module established in the step 3, and training the model; and 5, inputting a vibration signal under another working condition into the fault diagnosis model with the trained network parameters, carrying out fault state identification, and outputting a fault label of the to-be-diagnosed equipment to obtain a fault type. According to the method, higher fault identification capability and generalization capability can be obtained under variable working conditions, and the problem of poor generalization performance of a traditional convolutional neuralnetwork model under variable working conditions is solved.

Description

technical field [0001] The patent of the present invention relates to the field of fault diagnosis of mechanical equipment, in particular to a convolutional neural network fault diagnosis method based on a multi-channel attention module. Background technique [0002] With the improvement of production automation, informatization and intelligence, enterprises have higher and higher requirements for equipment reliability. One of the effective ways to improve the reliability of equipment is to carry out predictive maintenance on the equipment. This method monitors the condition of the equipment, collects the vibration signal during its working process, and judges the cause of the failure and the location of the failure based on the results of the analysis of the vibration signal. , and take corresponding measures in time to ensure the normal operation of the equipment and avoid economic losses caused by equipment failure. [0003] Existing machine learning model fault diagnosi...

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Problems solved by technology

[0011] The purpose of the present invention is to overcome the deficiencies in the prior art, aiming at the variable working condition problem of fault diagnosis, provide a convolutional neural network fault diagnosis method based on multi-channel attention module, which is used to solve the problem of traditional convolutional neural network model The problem of poor generalization under variable working conditions

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