层叠P阶多项式主成分分析在轴承故障诊断中的应用

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Abstract The traditional rolling bearing fault feature extraction and recognition highly rely on priori knowledges and expert experiences,resulting in its high labor cost and not enough accurate classification.A method of stacked P-order polynomial principal component analysis(SPPCA) was proposed to realize the accurate diagnosis of rolling bearing faults.First,a P-order polynomial principal component analysis(PPCA),which is applicable to deal with linear inseparable data,was presented to automatically learn the uncorrelated low-dimensional features from the vibration signals of rolling bearings.Next,a SPPCA network was built to further learn more discriminative features，using the back-forward optimization to ensure that learnt features are not distorted.Then,a K nearest neighbor classifier was used to classify the learnt feature vectors to identify the fault model.The experimental results on the database of rolling bearings faults verified the reliability and validity of the proposed method.

Key words： stacked learning stacked P-order polynomial principal component analysis rolling bearings fault diagnosis

Received: 07 September 2017 Published: 15 January 2019

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	Articles by authors
	MOU Liang
	WANG Kai
	LI Yan
	YU Hui

Cite this article:

MOU Liang,WANG Kai,LI Yan, et al. Bearing fault diagnosis based on the stacked P-order polynomial principal component analysis[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(2): 25-32.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I2/25

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