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Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

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Abstract

The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intense noise. Therefore, it is still a great challenge to make early fault diagnosis. An intelligent early fault diagnosis method for rotating machinery is proposed based on the parameter optimization of the variational mode decomposition (VMD) and deep multi-kernel extreme learning machine (DMKELM). Firstly, the improved whale optimization algorithm (IWOA) is designed by introducing the iterative chaotic mapping, nonlinear convergence factor and inertia weight to optimize the VMD parameters. Secondly, the optimized VMD (OVMD) with sample entropy is created to reduce noise and reconstruct the signals. Finally, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function, which can enhance the classification performance and generalization ability of the model. Two experiments on bearings and gears show that the fault diagnosis accuracy by DMKELM is 99 and 98.5%, respectively, which is at least 1% higher than comparative methods and increases by 4% after noise reduction. The result shows that the proposed method has great superiority in the early fault diagnosis of rotating machinery.

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Data Availability

The data supporting this study came to the laboratory of the School of Mechanical Engineering, Guangxi University. The data are reliable and can be provided free of charge only if the College of Mechanical Engineering allows it.

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Acknowledgements

The research was supported by the National Natural Science Foundation of China [Grant Number U22A2053, 52072081], the Major Science and Technology Project of Guangxi Province of China [Grant Number AA20302010], Interdisciplinary Scientific Research Foundation of Guangxi University [Grant Number 2022JCA003],Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund [Grant Number 19-050-44-S015], Innovation Project of Guangxi Graduate Education [Grant Number YCBZ2022043], Guangxi Key R & D Program [Grant Number 2021AB22124] and Project of Improving the Basic Ability of Scientific Research of Young and Middle-aged Teachers in Guangxi Universities [Grant Number 2022KY1134].

Funding

The funding was provided by the National Natural Science Foundation of China [Grant Number 52072081], the Major Science and Technology Project of Guangxi Province of China [Grant Number AA20302010], Interdisciplinary Scientific Research Foundation of Guangxi University [Grant Number 2022JCA003],Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund [Grant Number 19-050-44-S015], Innovation Project of Guangxi Graduate Education [Grant Number YCBZ2022043], Guangxi Key R & D Program [Grant Number 2021AB22124] and Project of Improving the Basic Ability of Scientific Research of Young and Middle-aged Teachers in Guangxi Universities [Grant Number 2022KY1134].

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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, Guangxi University, Nanning, 530004, China

    Zhenzhen Jin, Deqiang He, Zhenpeng Lao & Zexian Wei

  2. School of Quality and Standardization, Qingdao University, Qingdao, 266071, China

    Xianhui Yin

  3. Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou, 412001, China

    Weifeng Yang

Authors
  1. Zhenzhen Jin
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  2. Deqiang He
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  3. Zhenpeng Lao
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  4. Zexian Wei
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  5. Xianhui Yin
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  6. Weifeng Yang
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Contributions

Deqiang He analyzed the fault data of bearing. Zhenzhen Jin was the main author of the manuscript to analyze and explain the fault diagnosis of bearing. All authors read and approve final submissions.

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Correspondence to Deqiang He.

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The author(s) declared no potential conflicts of interest concerning the research, authorship, and/or publication of this paper.

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The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Guangxi University.

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Jin, Z., He, D., Lao, Z. et al. Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM. Nonlinear Dyn 111, 5287–5306 (2023). https://doi.org/10.1007/s11071-022-08109-8

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  • DOI: https://doi.org/10.1007/s11071-022-08109-8

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