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A method for condition monitoring and fault diagnosis in electromechanical system

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Abstract

Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localization. In this paper, the feasibility of using a nonlinear feature extraction method noted as Kernel independent component analysis (KICA) is studied and it is applied in self-organizing map to classify the faults of induction motor. In nonlinear feature extraction, we employed independent component analysis (ICA) procedure and adopted the kernel trick to nonlinearly map the Gaussian chirplet distributions into a feature space. First, the adaptive Gaussian chirplet distributions are mapped into an implicit feature space by the kernel trick, and then ICA is performed to extract nonlinear independent components of the Gaussian chirplet distributions. A thorough laboratory study shows that the diagnostic methods provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.

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Authors and Affiliations

  1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, 110016, China

    Qianjin Guo, Haibin Yu, Jingtao Hu & Aidong Xu

  2. Graduate School of the Chinese Academy of Sciences, Beijing, 100039, China

    Qianjin Guo

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  1. Qianjin Guo
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  2. Haibin Yu
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  3. Jingtao Hu
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  4. Aidong Xu
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Correspondence to Qianjin Guo.

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Guo, Q., Yu, H., Hu, J. et al. A method for condition monitoring and fault diagnosis in electromechanical system. Neural Comput & Applic 17, 373–384 (2008). https://doi.org/10.1007/s00521-007-0128-4

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  • DOI: https://doi.org/10.1007/s00521-007-0128-4

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