基于UDGDP的转子故障数据集降维方法

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Abstract Aiming at the problem of low accuracy in intelligent fault identification of rotating machinery, a dimension reduction algorithm based on uncorrelated double graphs discriminant projection (UDGDP) was proposed. The algorithm constructed two manifold structure graphs to make the intra-class samples more compact and the inter-class samples more separable in the low-dimensional space. At the same time, uncorrelated constraints were introduced to reduce the statistical correlation between feature components after projection transformation, and then the purpose of extracting sensitive fault features was achieved. The results of verification with rotor fault data set show that UDGDP algorithm can reduce the correlation between the features in the low dimensional space, and make the difference between the fault categories clearer, which effectively improves the identification accuracy of the classifier. The algorithm can provide a theoretical reference for the intelligent fault identification technology of rotor system.
Key words: double graphs discriminant projection; uncorrelated constraints; rotor fault data set; dimension reduction

Key words： double graphs discriminant projection uncorrelated constraints rotor fault data set dimension reduction

Received: 12 May 2021 Published: 28 August 2022

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	Articles by authors
	YANG Zeben
	ZHAO Rongzhen
	LIU Qiang

Cite this article:

YANG Zeben,ZHAO Rongzhen,LIU Qiang. A dimension reduction method for rotor fault data set based on UDGDP[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(16): 255-260.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I16/255

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