Self-adaptive sparse compression self-coding rolling bearing fault diagnosis system

Abstract

The invention discloses a self-adaptive sparse compression self-coding rolling bearing fault diagnosis system. The system comprises the following steps of firstly, acquiring and processing vibration signals at a rolling bearing, converting the acquired vibration signals into frequency domain signals, and then dividing the converted frequency spectrum signals into a training sample set and a test sample set; inputting the training sample into constructed self-adaptive sparse compression self-coding for characteristic learning so as to mine multilayer sensitive characteristics which are hidden in the data and have discriminability; finally, inputting the extracted multilayer sensitive characteristics into an unsupervised extreme learning machine optimized by a cuckoo search algorithm to train a classifier; and inputting the test sample set into a trained fault diagnosis system to perform unsupervised fault state separation and diagnosis. The method is simple and easy to implement, and the defects that the traditional deep learning fault diagnosis system is supervised in the classification stage and has low training efficiency can be avoided.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis in mechanical equipment, and is an intelligent rolling bearing fault diagnosis system based on deep learning. Background technique [0002] At present, rotating machinery equipment plays an indispensable and important role in the fields of industrial production and intelligent manufacturing, and plays an irreplaceable positive role. At the same time, the health maintenance and operation management of mechanical equipment has attracted the attention of more and more enterprises and R&D personnel. That is, effective and appropriate mechanical equipment condition monitoring and fault diagnosis can not only ensure the safe operation of rotating mechanical equipment, but also reduce unnecessary failures, increase the service life of mechanical equipment, and improve the economic benefits of the entire industrial system. [0003] In order to realize the effective diagnosis of the core component...

AI Technical Summary

Problems solved by technology

[0004] Most of the fault diagnosis methods based on signal analysis are difficult to quantify the fault diagnosis results, and most of the vibration signals collected by sensing and measuring equipment are unmarked and unknown.

Therefore, the rotating machinery fault diagnosis method based on vibration signal analysis is limited in the process of effective identification and analysis of fault diagnosis results.

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