基于关联维数和线段聚类的滚动轴承故障诊断

doi:10.3969/j.issn.1000-1158.2019.01.16

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Abstract A new fault feature extraction method for rolling bearing based on correlation dimension is proposed. Linear object recognition region is an important factor for the accuracy of correlation dimension, this method is characterized by the fluctuation of the two order derivative corresponding to the linear scaling region of the correlation dimension,the two order derivative data points are transformed into line segments,then the two clustering analysis is done by line segment clustering method, and the gross error is eliminated by statistical rule.Finally, the eigenvalues are fitted to data.This method is used to simulate and analyze the classical Lorenz chaotic system, and the effect is good. Four kinds of signal of rolling bearing are identified, and the experiment shows that the new method can identify the bearing fault signal more accurately.

Key words： metrology rolling bearing fault diagnosis correlation dimension line segment clustering

Received: 26 February 2018 Published: 07 January 2019

PACS:

TB936

Corresponding Authors: Zong MENG E-mail: mzysu@ysu.edu.cn

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	MENG Zong
	XING Ting-ting
	ZHANG Yuan-yuan
	ZHOU Ming-jun
	YIN Na

Cite this article:

MENG Zong,XING Ting-ting,ZHANG Yuan-yuan, et al. Fault Diagnosis of Rolling Bearing Based on Correlation Dimension and Segment Clustering[J]. Acta Metrologica Sinica, 2019, 40(1): 100-105.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.01.16 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I1/100

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