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Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

Author

Listed:
  • Mingsheng Wang

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China)

  • Wuxuan Lai

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China)

  • Hong Zhang

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China)

  • Yang Liu

    (College of Mechanical Electrification Engineering, Tarim University, Alar 843300, China)

  • Qiang Song

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China)

Abstract

The permanent magnet synchronous motor (PMSM) plays an important role in the power system of agricultural machinery. Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. The contributions of this paper can be summarized in three main aspects. Firstly, synchronizing data from different signals extracted by different devices presents a significant challenge. To address this, a signal synchronization method based on maximum cross-correlation is proposed to construct a synchronized dataset of current and vibration signals. Secondly, applying a traditional CNN to the data fusion of different signals is challenging. To solve this problem, a multi-stream high-level feature fusion algorithm based on a channel attention mechanism is proposed. Thirdly, to tackle the issue of hyperparameter tuning in deep learning models, a hyperparameter optimization method based on Bayesian optimization is proposed. Experiments are conducted based on the derived early-stage ITSC fault-severity indicator, validating the effectiveness of the proposed fault-diagnosis algorithm.

Suggested Citation

  • Mingsheng Wang & Wuxuan Lai & Hong Zhang & Yang Liu & Qiang Song, 2024. "Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization," Agriculture, MDPI, vol. 14(12), pages 1-27, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2139-:d:1529154
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    References listed on IDEAS

    as
    1. Xing-Hua Yuan & Yu-Ling He & Man-Yu Liu & Hui Wang & Shu-Ting Wan & Gaurang Vakil, 2021. "Impact of the Field Winding Interturn Short-Circuit Position on Rotor Vibration Properties in Synchronous Generators," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, November.
    2. Quentin Lagarde & Bruno Beillard & Daan Marcuzzi & Serge Mazen & Julien Leylavergne, 2023. "Stray Currents in Livestock Farming: Electrical Diagnosis in Farms," Agriculture, MDPI, vol. 13(10), pages 1-17, October.
    3. Yu-Ling He & Tao Wang & Kai Sun & Xiao-Long Wang & Bo Peng & Shu-Ting Wan, 2020. "Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, April.
    4. Jiabo Wang & Zhixiong Lu & Guangming Wang & Ghulam Hussain & Shanhu Zhao & Haijun Zhang & Maohua Xiao, 2023. "Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN," Agriculture, MDPI, vol. 13(2), pages 1-17, February.
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