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A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing

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Abstract

A novel classification method based on ICGOA-KELM is presented in this paper. In ICGOA-KELM, an improved circle chaotic map with grasshopper optimization algorithm (ICGOA) is designed to optimize the parameters of Kernel extreme learning machine (KELM) to improve the stability and accuracy of fault classification for rolling bearing based on parameter modification of circle chaotic map. Grasshopper optimization algorithm (GOA) is a new heuristic optimization algorithm, which has strong global searching ability. However, it still may fall into local optimization in some cases. In this paper, the vibration signals of rolling bearing are preprocessed by using Variational Modal Decomposition (VMD). Then Multi-scale Permutation Entropy (MPE) is utilized to extracted features of intrinsic mode functions (IMFs) decomposed by VMD. In addition, KPCA is adopted to select the salient features with high contribution rates to remove redundant and irrelevant features. Finally, the salient features are fed into ICGOA-KELM to fulfill fault classification. Therefore, a new fault detection and classification method based on VMD, MPE, KPCA and ICGOA-KELM is proposed. This method is applied to the fault classification of rolling bearing and the identification of different damage fault degrees. Experiments verify that the proposed method is more effective than CGOA-KELM for fault diagnosis of rolling bearing.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NO.61763029, 61873116), the Open Foundation of State Key Laboratory of Large Electric Drive System and Equipment Technology (No. SKLLDJ012016020), the Industrial support and guidance project of colleges and universities of Gansu Province (No. 2019C-05) and the open fund project of Key Laboratory of Gansu Advanced Control for Industrial Processes (No. 2019KFJJ01). The authors would like to thank Case Western Reserve University for providing motor bearing vibration data.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Peng Chen & Xiaoqiang Zhao

  2. College of Electrical and Electronic Engineering, Lanzhou Petrochemical Polytechnic, Lanzhou, 730060, China

    Peng Chen

  3. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou, 730050, China

    Xiaoqiang Zhao

  4. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Xiaoqiang Zhao

  5. State Key Laboratory of Large Electric Drive System and Equipment Technology, Tianshui, 741020, China

    Qixian Zhu

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  1. Peng Chen
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  2. Xiaoqiang Zhao
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Correspondence to Xiaoqiang Zhao.

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Chen, P., Zhao, X. & Zhu, Q. A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing. Appl Intell 50, 2833–2847 (2020). https://doi.org/10.1007/s10489-020-01684-6

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