A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data

Jing Yang; Guo Xie; Yanxi Yang; Qijun Li; Cheng Yang; Jing Yang; Guo Xie; Yanxi Yang; Qijun Li; Cheng Yang

doi:10.3934/mbe.2023242

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 5223-5242. doi: 10.3934/mbe.2023242

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Research article

A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data

1.
School of Mechatronics and Automotive Engineering, Tianshui Normal University, Tianshui 741000, China
2.
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China

Academic Editor: Yang Kuang

Received: 28 October 2022 Revised: 05 December 2022 Accepted: 20 December 2022 Published: 09 January 2023

As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.
- regulable resampling,
- multilevel recovery,
- sparse autoencoder,
- rolling bearing,
- fault diagnosis
Citation: Jing Yang, Guo Xie, Yanxi Yang, Qijun Li, Cheng Yang. A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 5223-5242. doi: 10.3934/mbe.2023242

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Abstract

As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.

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