基于独立特征选择与流形学习的故障诊断

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Abstract In order to diagnose fault effectively by using sensitive features contained in a feature set, a fault diagnosis method based on individual feature selection (IFS) and manifold learning was proposed.Firstly, the mixed feature of the vibration signal was extracted from multiple domains, and the original high-dimensional feature set was constructed.Then, an improved kernel Fisher feature selection method was proposed and used to select individual sensitive feature subset for each pair of classes, and the mining performance of the feature subset with higher distinguishability was further implemented by using Linear local tangent space alignment (LLTSA).Finally, a one-against-one approach was applied to train several SVM binary classifiers, and low-dimensional feature was input into the multi-class fault diagnosis model for recognizing the fault types.The experimental results of hydraulic pump indicate that the proposed method is of high diagnostic accuracy.
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Key words： Fault diagnosis Individual feature selection Kernel Fisher discriminant analysis Manifold learning

Received: 10 March 2017 Published: 15 August 2018

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	Articles by authors
	DU Wei
	FANG Liqing
	QI Ziyuan

Cite this article:

DU Wei,FANG Liqing,QI Ziyuan. Fault diagnosis based on individual feature selection and manifold learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(16): 77-83.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2018/V37/I16/77

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