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Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework

Author

Listed:
  • Moritz Benninger

    (Faculty of Electronics and Computer Science, University of Applied Sciences Aalen, 73430 Aalen, Germany)

  • Marcus Liebschner

    (Faculty of Electronics and Computer Science, University of Applied Sciences Aalen, 73430 Aalen, Germany)

  • Christian Kreischer

    (Chair for Electrical Machines and Drive Systems, Helmut Schmidt University, 22043 Hamburg, Germany)

Abstract

This paper deals with the early detection of fault conditions in induction motors using a combined model- and machine-learning-based approach with flexible adaptation to individual motors. The method is based on analytical modeling in the form of a multiple coupled circuit model and a feedforward neural network. In addition, the differential evolution algorithm independently identifies the parameters of the motor for the multiple coupled circuit model based on easily obtained measurement data from a healthy state. With the identified parameters, the multiple coupled circuit model is used to perform dynamic simulations of the various fault cases of the specific induction motor. The simulation data set of the stator currents is used to train the neural network for classification of different stator, rotor, mechanical, and voltage supply faults. Finally, the combined method is successfully validated with measured data of faults in an induction motor, proving the transferability of the simulation-trained neural network to a real environment. Neglecting bearing faults, the fault cases from the validation data are classified with an accuracy of 94.81%.

Suggested Citation

  • Moritz Benninger & Marcus Liebschner & Christian Kreischer, 2023. "Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework," Energies, MDPI, vol. 16(8), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3429-:d:1122840
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    References listed on IDEAS

    as
    1. Ana L. Martinez-Herrera & Edna R. Ferrucho-Alvarez & Luis M. Ledesma-Carrillo & Ruth I. Mata-Chavez & Misael Lopez-Ramirez & Eduardo Cabal-Yepez, 2022. "Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation," Energies, MDPI, vol. 15(4), pages 1-11, February.
    2. Alasmer Ibrahim & Fatih Anayi & Michael Packianather & Osama Ahmad Alomari, 2022. "New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection," Energies, MDPI, vol. 15(4), pages 1-24, February.
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    Cited by:

    1. Reza Bazghandi & Mohammad Hoseintabar Marzebali & Vahid Abolghasemi & Shahin Hedayati Kia, 2023. "A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines," Energies, MDPI, vol. 16(14), pages 1-17, July.
    2. Moritz Benninger & Marcus Liebschner, 2024. "Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors," Energies, MDPI, vol. 17(15), pages 1-21, July.

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