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A new comprehensive automatic fault detection method for rotating machinery using HmvAAPE and VNWOA-KELM

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Abstract

In order to efficiently and automatically identify the faults of rotating machinery, so as to avoid the dangers and losses caused by them, this paper proposes a new fault feature extraction method for rotating machinery named Hierarchical Multi-variate Amplitude-aware Permutation Entropy (HmvAAPE), which has integrated the advantages of Amplitude-aware Permutation Entropy (AAPE), multi-channel analysis method and hierarchical decomposition method. Therefore, the features extracted by this feature extraction method can contain more complete fault information. The t-SNE algorithm is chosen to conduct dimensional reduction of features and the Kernel Extreme Learning Machine optimized by Von Neumann Topology Whale Optimization Algorithm (VNWOA-KELM) is proposed to learn fault characteristics and classify faults automatically. By designing bearing and gearbox fault experiments and collecting their fault data to verify the effectiveness of the proposed method, it can be obtained that the average classification accuracy of this method can reach 98.9%. Through comparative experiments, conclusions can be made that this method can get both higher accuracy and higher stability at the same time.

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Authors and Affiliations

  1. Field engineering college, Army Engineering University of PLA, Nanjing, 210007, Jiangsu, People’s Republic of China

    Jiancheng Gong, Xiaoqiang Yang, Jinxing Shen, Fuming Zhou & Wuqiang Liu

  2. The First Scientific Research Institute of Wuxi, Wuxi, 214000, Jiangsu, People’s Republic of China

    Jun Han

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  1. Jiancheng Gong
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  2. Xiaoqiang Yang
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  3. Jun Han
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Correspondence to Jun Han.

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Gong, J., Yang, X., Han, J. et al. A new comprehensive automatic fault detection method for rotating machinery using HmvAAPE and VNWOA-KELM. Appl Intell 53, 204–225 (2023). https://doi.org/10.1007/s10489-022-03505-4

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