[1] Tong Qingjun, Hu Jianzhong, Jia Minping, Xu Feiyun, et al. Rolling bearing performance degradation evaluationby VMD and embedding selection-based NPE [J]. Journal of Southeast University (English Edition), 2019, 35 (4): 408-416. [doi:10.3969/j.issn.1003-7985.2019.04.002]

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Rolling bearing performance degradation evaluationby VMD and embedding selection-based NPE()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

35

Issue:

2019 4

Page:

408-416

Research Field:

Mechanical Engineering

Publishing date:

2019-12-30

Info

Title:

Rolling bearing performance degradation evaluationby VMD and embedding selection-based NPE

Author(s):

Tong Qingjun;  Hu Jianzhong;  Jia Minping;  Xu Feiyun

School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

performance degradation evaluation;  variational mode decomposition(VMD);  neighborhood preserving embedding(NPE);  support vector data description(SVDD)

PACS:

TH17

DOI:

10.3969/j.issn.1003-7985.2019.04.002

Abstract:

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process, an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed. Firstly, the acquired vibration signals are decomposed by variational mode decomposition(VMD), and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set. Then, the NPE manifold learning method is used to extract the embedded features in the feature space. Considering the problem that useful embedding information is easily suppressed in NPE, an embedding selection strategy is built based on the Spearman correlation coefficient. The effectiveness of embeddings is measured by the coefficient absolute value, and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method. Finally, the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved. The proposed method was tested with the whole life experiment data of a rolling bearing, and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE. The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

References:

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Memo

Memo:

Biographies: Tong Qingjun(1994—), male, graduate; Hu Jianzhong(corresponding author), male, doctor, associate professor, hjz@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51975117).
Citation: Tong Qingjun, Hu Jianzhong, Jia Minping, et al. Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE[J].Journal of Southeast University(English Edition), 2019, 35(4):408-416.DOI:10.3969/j.issn.1003-7985.2019.04.002.

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