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Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

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Abstract

High-dimensional and unlabeled data collected from multi-sensor is a common scenario in practical production. The fault diagnosis and health stage (HS) division of bearing under different degradation processes is easily limited due to unlabeled and high-dimensional data. This work designs an intelligent fault diagnosis and HS division strategy for unlabeled and high-dimensional data. An adaptive tensor density peaks search (ATDPS) clustering algorithm is proposed for the HS division of rolling bearing. Moreover, to enhance the clustering performance, a novel neighborhood least square (NLS) technique is developed for feature space denoising, whose effectiveness and superiority are verified compared with the other feature space denoising techniques. The proposed strategies are subsequently applied to three benchmark cases and compared with other clustering methods. The experiment results demonstrate that the proposed strategy can reliably and accurately divide the different degradation stages depending on less prior knowledge. Furthermore, the presented HS division approach successfully monitors degradation with compound failure, showing potential for practical application.

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Data availibility statement

The data in Case 1 is from our laboratory facility and is not publicly available unless requested and approved by us.

Abbreviations

HS:

Health Stage

RUL:

Remaining Useful Life

NLS:

Neighborhood Least Square

DPS:

Density Peaks Search

ADPS:

Adaptive Density Peaks Search

ATDPS:

Adaptive Tensor Density Peaks Search

PAM:

Partition Around Medoids

SVD:

Singular Value Decomposition

VMD:

Variational Mode Decomposition

DDS:

Drivetrain Dynamics Simulator

TF:

Statistics Feature in Time Domain

FF:

Statistics Feature in Frequency Domain

IF:

Inner Race Fault

OF:

Outer Race Fault

BF:

Ball Fault

N:

Normal

N.D.:

Naturally Degraded

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant No. U22A2053], the Major Project of Science and Technology of Guangxi Province of China [Grant No. AA20302010], the Interdisciplinary Scientific Research Foundation of Guangxi University [Grant No. 2022JCA003] and the Innovation Project of Guangxi Graduate Education [Grant No. YCBZ2023039].

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Authors and Affiliations

  1. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, School of Mechanical Engineering of Guangxi University, Nanning, 530004, Guangxi, China

    Zexian Wei, Deqiang He, Zhenzhen Jin, Xueyan Zou, Jian Miao & Chang Liu

  2. CRRC Zhuzhou Institute Co.,Ltd., Zhuzhou, 412001, Hunan, China

    Sheng Shan

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  1. Zexian Wei
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  2. Deqiang He
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  7. Chang Liu
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Correspondence to Deqiang He.

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Wei, Z., He, D., Jin, Z. et al. Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising. Appl Intell 53, 24671–24688 (2023). https://doi.org/10.1007/s10489-023-04843-7

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