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Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Author

Listed:
  • Jie Ma

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
    Department of Railway Rolling Stock, Liaoning Railway Vocational and Technical College, Jinzhou 121000, China)

  • Yingxue Li

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Liying Wang

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Jisheng Hu

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Hua Li

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Jiyou Fei

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Lin Li

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Geng Zhao

    (College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China)

Abstract

The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δ i and phase angle difference Δ θ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δ i and phase angle features Δ θ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

Suggested Citation

  • Jie Ma & Yingxue Li & Liying Wang & Jisheng Hu & Hua Li & Jiyou Fei & Lin Li & Geng Zhao, 2023. "Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest," Energies, MDPI, vol. 16(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4949-:d:1179538
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    References listed on IDEAS

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    1. Dimitrios A. Papathanasopoulos & Konstantinos N. Giannousakis & Evangelos S. Dermatas & Epaminondas D. Mitronikas, 2021. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives," Energies, MDPI, vol. 14(8), pages 1-24, April.
    2. Cherif, Hakima & Benakcha, Abdelhamid & Laib, Ismail & Chehaidia, Seif Eddine & Menacer, Arezky & Soudan, Bassel & Olabi, A.G., 2020. "Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor," Energy, Elsevier, vol. 212(C).
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