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Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm

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Abstract

This study develops an electrical detection method for the diagnosis and fault detection of induction motors. An experiment constructs two types of defect models: broken bar and dynamic eccentricity. Electrical signals acquired during the operation of a motor are transformed through a fast Fourier transform to obtain the feature frequency components for identifying the type of motor fault. Subsequently, the Clark-Concordia transform is used to compare the stator current Concordia pattern between faulty and healthy motors. Finally, a fuzzy inference system is designed for assessing the severity of motor faults. The proposed method not only can diagnose the type of motor fault, but can also assess the operational state of a motor. The method is suitable for preparing a maintenance program for induction motors and for reducing their excessive maintenance cost.

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Acknowledgments

The research was supported by the Ministry of Science and Technology of the Republic of China, under Grant No. MOST 103-2221-E-011-077-MY2.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei, 10607, Taiwan

    Hong-Chan Chang, Shang-Chih Lin, Cheng-Chien Kuo & Cheng-Fu Hsieh

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  1. Hong-Chan Chang
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  2. Shang-Chih Lin
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  3. Cheng-Chien Kuo
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Correspondence to Hong-Chan Chang.

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Chang, HC., Lin, SC., Kuo, CC. et al. Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm. Int. J. Fuzzy Syst. 18, 732–740 (2016). https://doi.org/10.1007/s40815-016-0199-4

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  • DOI: https://doi.org/10.1007/s40815-016-0199-4

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