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Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors

Author

Listed:
  • Moritz Benninger

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

  • Marcus Liebschner

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

Abstract

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3723-:d:1444808
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    References listed on IDEAS

    as
    1. 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.
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