基于IITD模糊熵与随机森林的滚动轴承故障诊断方法

doi:10.3969/j.issn.1000-1158.2021.06.13

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Abstract Aiming at the problem that it is difficult to identify the weak vibration signal feature of rolling bearing faults, a rolling bearing fault diagnosis method based on improved intrinsic time scale decomposition (IITD) and fuzzy entropy (FE) input random forest (RF) pattern recognition was proposed. First, the bearing test bench was used to collect the vibration signals of the bearing in four states: normal, rolling body failure, inner ring failure, and outer ring failure; the collected vibration signals were decomposed into a set of proper rotation components (PRC) by IITD decomposition, Then selected the effective component that represents the main information of the fault to calculate its fuzzy entropy value and constructed the feature vector, which was input to a random forest classifier model for identification and classification. The experimental data analysis results show that this method can effectively realize the diagnosis of rolling bearing fault categories.

Key words： metrology rolling bearing intrinsic time scale decomposition fuzzy entropy random forest fault diagnosis

Received: 05 January 2020 Published: 23 June 2021

PACS:

TB936

Fund:;Monitoring and early warning system for high-speed railway operation safety vehicle fault based on acoustic vibration signal feature recognition

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	Articles by authors
	CHEN Jian
	CAI Kun-qi
	TAO Shan-yong
	YANG Bin
	LIU Yuan-yuan
	LIU Xing-fu
	HUANG Kai-xuan

Cite this article:

CHEN Jian,CAI Kun-qi,TAO Shan-yong, et al. Fault Diagnosis Method of Rolling Bearing Based on IITD Fuzzy Entropy and Random Forest[J]. Acta Metrologica Sinica, 2021, 42(6): 774-779.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.06.13 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I6/774

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