Fuzzy granulation prediction method of performance degradation of rolling bearing on the basis of information entropy

Abstract

The invention discloses a fuzzy granulation prediction method which specially aims at the performance degradation tendency of a rolling bearing. The method comprises the following steps: firstly, decomposing the vibration signal sequence of the rolling bearing by EEMD (Ensemble Empirical Mode Decomposition), and extracting the performance degradation index sequence of the vibration signal of the rolling bearing by a Shannon entropy theory; then, utilizing a fuzzy information granulation theory to carry out fuzzy information granulation on the performance degradation index sequence; and finally, inputting granulated granular information into an LS-SVM (Least Squares Support Vector Machine) to carry out regression prediction. The fuzzy granulation prediction method gives full play to the advantages of the EEMD on the aspect of the performance degradation gradual change information extraction of the rolling bearing, the advantages of information entropy on the aspect of information mining, the advantages of the LS-SVM on the aspect of regression prediction and the like, can predict the performance degradation tendency of the rolling bearing in a service cycle and also can effectively predict the performance change fluctuation range of the rolling bearing in one service time period.

Description

technical field [0001] The invention relates to a method for predicting the performance degradation trend of a rolling bearing, in particular to a fuzzy granulation prediction method for the performance degradation trend of a rolling bearing based on Shannon information entropy and fuzzy information granulation. Background technique [0002] The performance degradation of rolling bearings is the main problem that threatens the safe service of rotating machinery. If the service performance of rolling bearings can be reliably predicted and its change trend can be known in advance, the occurrence of vicious events such as cumulative overrun of dangerous factors and sudden shutdown of rotating machinery can be avoided. . In a broad sense, equipment performance degradation prediction is aimed at identifying the current operating state of the equipment, analyzing the causes of the current state of the equipment, predicting the future development trend of the equipment, and proposi...

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Problems solved by technology

[0005] In order to solve the theoretical problems such as difficult feature extraction and difficult model construction in the process of predicting the performance degradation trend of rolling bearings, the present invention proposes a fuzzy granulation prediction method for rolling bearing performance degradation based on information entropy. The vibration signal of the time history series is processed, and the energy spectrum entropy sequence of the vibration signal is obtained based on the Shannon information entropy theory. The granulation information of the rolling bearing is input to the prediction model, and finally the effective prediction of the degradation trend of the rolling bearing performance is realized

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