Bearing health state monitoring method based on convolution auto-encoder

Abstract

The invention provides a bearing health state monitoring method based on a convolutional auto-encoder, and belongs to the field of bearing fault prediction and health management. The method comprises the following steps: firstly, acquiring a full-life-cycle digital vibration signal and a health state marking value of a brand new bearing; extracting intrinsic mode component statistic characteristics of the digital vibration signals and depth characteristics learned by using a convolutional auto-encoder, splicing and screening the two characteristics, inputting the screened characteristics into a full-connection regression network for regression training, and finally obtaining a health state curve graph of the bearing in the full life cycle; then, obtaining a current health state curve graph of the same type of to-be-monitored bearing; and comparing the two curve graphs to obtain a health state monitoring result of the to-be-monitored bearing. According to the method, the integrity of the bearing vibration signal features is improved through the convolution self-encoder, redundant features are removed by using a feature sorting and feature selection method, and the relatively accurate bearing health state can be obtained.

Description

technical field [0001] The invention belongs to the field of bearing fault prediction and health management, and in particular relates to a method for monitoring the health state of bearings based on a convolutional autoencoder. Background technique [0002] Bearings are an essential part of all kinds of rotating machinery. The health of bearings directly affects the operation of the entire rotating machinery system, especially in large-scale machinery scenarios, such as aircraft, wind turbines, elevators and other mechanical systems. Health problems of bearing components may cause great loss of life and property. Therefore, in recent years, the health status monitoring and failure prediction of bearings have received more and more attention. For the monitoring of bearing health status in rotating machinery, we cannot obtain it through direct measurement or intuitive estimation. At present, the more commonly used monitoring methods mostly obtain the health status of the be...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of complex bearing vibration signals, incomplete feature extraction, and feature redundancy in the health status monitoring of bearing components in existing rotating machinery equipment, and propose a bearing based on convolutional self-encoder Health Status Monitoring Method

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