Rolling bearing fault diagnosis method based on VMD and deep convolutional neural network

Abstract

The invention relates to a rolling bearing fault diagnosis method based on VMD and a deep convolutional neural network. The rolling bearing fault diagnosis method comprises the steps of 1, original vibration data of a rolling bearing is collected; 2, variational mode decomposition data processing and neural network training are carried out on the training set vibration data; and step 3, variational mode decomposition is used to carry out data processing on the test vibration data, and a neural network is used to carry out fault diagnosis. For rolling bearing fault detection, a method of combining variational mode decomposition and a deep convolutional neural network is provided, and diagnosis of different fault types and damage degrees of the rolling bearing under the condition of variable working conditions is realized. Vibration data can be decomposed into different limited band eigenmode function components through variational mode decomposition, and a convolutional layer of the deep convolutional neural network can extract local features of each limited band eigenmode function from different angles, so that diversity and comprehensiveness of feature extraction are ensured.

Description

Technical field [0001] The present invention relates to the field of mechanical fault diagnosis, in particular, to a rolling bearing fault diagnosis method based on VMD and depth convolutional neural network. Background technique [0002] Rolling bearings are one of the most commonly indispensable general mechanical components in mechanical equipment. It is widely used in the fields of metallurgy, aerospace, chemical. The rolling bearing provides reliable support for the mechanical structure. Its operational state directly affects the safety and related performance of the equipment operation. Since the long-term repetition of the working surface of the rolling bearing and the long-term repetition of the contact stress, the rolling bearing is caused by high speed, high load, high temperature environment Extremely, loss, cracking, etc., resulting in an abnormality of the rolling bearing, however, due to the complex working condition during operation, the generated vibration signal ...

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Problems solved by technology

[0006] For this reason, the present invention provides a rolling bearing fault diagnosis method based on VMD and deep convolutional neural network, which is used to partially overcome the inability to take into account the global and localization of smooth signals in the time domain and frequency domain in the rolling bearing diagnosis in the prior art question

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