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Rolling Bearing Incipient Fault Detection via Optimized VMD Using Mode Mutual Information

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  • Control Theory and Applications
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Abstract

The complete failure of the rolling bearing is a deterioration process from the incipient weak fault to the severe fault, thus it is important to alarm when the incipient fault appear. This work presents a novel incipient bearing fault diagnosis framework using mode mutual information (MMI) based fitness function, variational mode decomposition (VMD), and cuckoo search (CS) algorithm. MMI based fitness function is proposed in order to obtain the optimal combinations of the VMD parameters. Therefore, the optimal parameters of VMD can be obtained by CS algorithm using proposed fitness function. Afterwards, a vibration signal is decomposed into a set of modes using the optimal VMD, and the kurtosis value of all modes are calculated. The envelop of the mode with maximum kurtosis value between modes and raw signal is computed as the input vector of the stacked denoised autoencoder (SDAE). Comparisons have been conducted via SDAE to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

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Funding

This research is supported by the National Natural Science Foundation of China (No.62073141), the National Key Research and Development Program of China (No.2020YFC1522505, No.2020YFC1522502), and Shanghai Natural Science Foundation (No.22ZR1417000).

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China

    Shuai Tan, Aimin Wang, Hongbo Shi & Lei Guo

Authors
  1. Shuai Tan
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  2. Aimin Wang
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  3. Hongbo Shi
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  4. Lei Guo
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Corresponding author

Correspondence to Shuai Tan.

Additional information

Shuai Tan received her B.S. degree in automation and a Ph.D. degree in control theory and control engineering from Northeastern University, China, in 2005 and 2012, respectively. She is currently an Associate Professor with the East China University of Science and Technology, Shanghai, China. Her research interests include operation state evaluation for complex industrial process, fault monitoring and diagnosis, and machine learning of image information.

Aimin Wang was born in 1997. He received his B.E. degree in automation major from East China University of Science and Technology, China, in 2019. He is currently pursuing a master’s degree in control science and engineering with the School of East China University of Science and Technology, Shanghai, China. His research interests include faults detection of mechanical systems.

Hongbo Shi received his B.E. and Ph.D. degrees in control engineering from East China University of Science and Technology, China, in 1982, and 2000, respectively. He has been a Professor with the School of Information Science and Engineering, East China University of Science and Technology, where he has also been the secretary, since 2013. He is the author of more than 150 articles (SCI/EI), and five patents. His current research interests include industrial process modeling, control and optimization.

Lei Guo was born in 1994. He received his B.S. degree in mechanical design manufacture and automation major from Jiamusi University, China, in 2017 and an M.S. degree in mechanical engineering from the Shenyang University of Technology in 2020. He is currently pursuing a doctoral candidate in control science and engineering with the School of East China University of Science and Technology, Shanghai, China. His research interests include faults detection of mechanical systems.

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Tan, S., Wang, A., Shi, H. et al. Rolling Bearing Incipient Fault Detection via Optimized VMD Using Mode Mutual Information. Int. J. Control Autom. Syst. 20, 1305–1315 (2022). https://doi.org/10.1007/s12555-021-0100-6

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  • DOI: https://doi.org/10.1007/s12555-021-0100-6

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