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Fault diagnosis of rotating machines based on EEMD-MPE and GA-BP

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Abstract

Vibration signals of rolling element bearings (REBs) contain substantial bearing motion state information. However, the property of nonlinear and nonstationary vibration signals decreases the diagnostic accuracy of REBs. To improve the accuracy of fault diagnosis for REBs, an ensemble approach based on ensemble empirical mode decomposition (EEMD), multi-scale permutation entropy (MPE), and backpropagation (BP) neural network optimized by genetic algorithm (GA) is proposed. Firstly, the REBs are decomposed into a set of intrinsic mode functions (IMFs) that contain various fault features by EEMD. The fault features of the first four IMFs are extracted by MPE, and the feature vectors are formed. Then, the BP neural network optimized by GA is utilized as a classifier for fault diagnosis to train and test the feature vector set, and the fault diagnosis of the REBs is realized in the form of probability output. Experimental results show that the proposed method can identify the fault pattern of the vibration signals of REBs precisely. Compared with the existing fault diagnosis methods, the proposed method can realize the fault diagnosis of REBs with 16 fault patterns, and demonstrates an excellent accuracy rate.

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Acknowledgements

Thanks for the editors, referees, and all the workmates who dedicated their precious time to this research and provided insightful suggestions. All their work contributes greatly to this article.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 51975249), Chongqing Natural Science Foundation project (cstc2021jcyj-msxm2142), Fundamental Research Funds for the Central Universities, JLUSTIRT, and Interdisciplinary Research Funding Program for Doctoral Students of Jilin University (Grant No. 101832020DJX034).

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Authors and Affiliations

  1. Key Laboratory of CNC Equipment Reliability, Ministry of Education, ChangchunJilin Province, 130022, China

    Tongtong Jin, Siyuan Wang, Jinyan Guo & Chuanhai Chen

  2. Key Laboratory of CNC Equipment Reliability, Ministry of Education, School of Mechanical and Aerospace Engineering, Jilin University, Ren Min str. 5988, ChangchunJilin Province, 130022, China

    Tongtong Jin, Siyuan Wang, Jinyan Guo & Chuanhai Chen

  3. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, 100124, China

    Qiang Cheng

  4. Dalian Kede Numerical Control Co., LTD, Dalian, 116602, China

    Hu Chen

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  1. Tongtong Jin
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  2. Qiang Cheng
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  3. Hu Chen
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  4. Siyuan Wang
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  5. Jinyan Guo
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Contributions

Tongtong Jin: background research, methodology, data curation, software, validation writing—original draft, editing.

Qiang Cheng: review and editing, supervision.

Hu Chen: software, review, and editing.

Siyuan Wang: review and editing.

Jinyan Guo: review and suggestion.

Chuanhai Chen: review and editing, supervision, project administration, funding acquisition.

Corresponding authors

Correspondence to Qiang Cheng or Chuanhai Chen.

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Jin, T., Cheng, Q., Chen, H. et al. Fault diagnosis of rotating machines based on EEMD-MPE and GA-BP. Int J Adv Manuf Technol 124, 3911–3922 (2023). https://doi.org/10.1007/s00170-021-08159-z

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  • DOI: https://doi.org/10.1007/s00170-021-08159-z

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