Clustering analysis-based intelligent fault diagnosis method for antifriction bearing of mechanical system

Abstract

The invention discloses a clustering analysis-based intelligent fault diagnosis method for an antifriction bearing of a mechanical system. A diagnosis model is trained firstly, comprising the following steps: collecting standard vibration signal samples of five fault and normal bearing states of an outer ring, an inner ring, a rolling body and a holding frame; decomposing signals, extracting original vibration signals as well as time domain and frequency domain characteristics of decomposed components to obtain an original characteristic set; removing redundancy by means of a self-weight algorithm and an AP (Affinity Propagation) clustering algorithm to obtain Z optimal characteristics; classifying sample statuses by means of the AP clustering algorithm to obtain a well-trained diagnosis model. A fault diagnosis is performed by the following steps: collecting real-time vibration information of a bearing, decomposing the signals, extracting the optimal characteristics determined by the model, importing the AP clustering algorithm to cluster parameters based on the diagnosis model, comparing with the Z characteristics known in the model to obtain a category of a current unknown signal, so as to complete the fault diagnosis. According to the clustering analysis-based intelligent fault diagnosis method disclosed by the invention, both EEMD (Ensemble Empirical Mode Decomposition) and WPT are utilized to decompose the vibration signals, more refined bearing status information can be acquired, the self-weight algorithm and the AP clustering algorithm increase intelligence of the diagnosis, and therefore accurate diagnosis is ensured.

Description

technical field [0001] The invention belongs to the field of fault monitoring and diagnosis of equipment systems, in particular to an intelligent fault diagnosis method for rolling bearings of mechanical systems based on cluster analysis. Background technique [0002] With the development of science and technology and the progress of society, various types of mechanical equipment have been widely used in engineering. Rolling bearings are the key components in mechanical equipment and also the most used components. Rolling bearings may be damaged due to various reasons during operation, such as improper assembly, poor lubrication, moisture and foreign matter intrusion, corrosion and overload, etc. may cause premature damage to rolling bearings. Even if the installation, lubrication and maintenance are normal, after a period of operation, the rolling bearings will have fatigue spalling, wear, pitting and other faults, which will cause the bearings to fail to work normally. G...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing intelligent fault diagnosis technologies only use a single indicator or a combination of several indicators to judge the type of fault during diagnosis.

However, when identifying the faults of complex objects, a few indicators sometimes cannot fully express the fault characteristics, so the accuracy of fault diagnosis is low

[0005] Although with the rapid development of signal processing and feature extraction technology, more and more features can participate in the diagnosis of faults, but it is difficult for existing diagnostic methods to classify signals and extract fault information features. Low efficiency, unable to complete real-time fault diagnosis

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