基于局部主成分保持投影的旋转机械故障数据降维方法

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Abstract Aiming at the classification difficulty caused by feature redundancy in high-dimensional fault feature set of rotating machinery, a dimension reduction algorithm of fault data set based on Local Principal Component Preserving Projection (LPCPP) is proposed. The algorithm integrates two ideas of inter-class separability criterion and principal component calculation with Locality Preserving Projection (LPP), which makes the algorithm have the ability to eliminate redundant features and reduce dimensionality reduction blindness, so that it can better retain the fault information with high value density and the main components of features that can reflect the operation status of machinery. The proposed algorithm is verified by the vibration signals of two different types of two span rotor systems, and the dimensionality reduction performance of the algorithm is evaluated by the separability index and the recognition accuracy of three different classifiers. The results show that the algorithm can achieve the function of reducing the difficulty of fault classification and improving the accuracy of fault classification, which can provide a theoretical basis for data computing to accumulate the data resources with high value density and the engineering implementation of intelligent decision-making technology for rotating machinery based on "industrial big data".

Key words： Fault diagnosis Local preserving projection Separability Principal component calculation Rotating machinery

Received: 25 November 2021 Published: 28 March 2023

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	Articles by authors
	YUAN Jianhui
	ZHAO Rongzhen
	MA Chi

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YUAN Jianhui,ZHAO Rongzhen,MA Chi. Dimension reduction method for rotating machinery fault data based on local principal component preserving projection[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(6): 24-30.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I6/24

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