滑动轴承摩擦故障趋势预测的系统自记忆模型

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Abstract The sliding bearing’s wear trend is hard to predict because its time series is nonlinear.To solve this problem,a wear trend prediction method for sliding bearing based on System self memory prediction model is put forward in this text.First,according to the different signal excitation source,the acquisition signal is separated into impact sound and random noise.Then the function fitting, derivation and grey theory are respectively inversed to get system differential equations of Impact sound and random sound.And Bidirectional Difference is used to obtain the influence coefficient of differential equation on the dynamic core of the bearing wear signal system.By introducing self memory function,the dynamic core of the bearing wear signal system is inversed into a Differential-Difference Equation.Then a self memory prediction model of sliding bearing is obtained.By applicating the proposed method in the case of sliding bearing wear degradation of static and dynamic loads,the validity of the method is verified.And it provides a new way to predict the degradation trend of sliding bearing.

Key words： plain bearing friction fault development trend nonlinear dynamical systems signal separation self-memorization model

Received: 04 March 2016 Published: 25 May 2017

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	Articles by authors
	ZHANG Jun-ning1
	ZHANG Pei-lin1
	CHUA Chun-rong2
	Wu Ding-hai1

Cite this article:

ZHANG Jun-ning1,ZHANG Pei-lin1,CHUA Chun-rong2, et al. Power system self-memory model for predicting the wear trend of sliding bearing[J]. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(11): 20-26.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2017/V36/I11/20

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