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

Moritz Benninger and Marcus Liebschner ()
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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

Energies, 2024, vol. 17, issue 15, 1-21

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.

Keywords: fault detection; induction motors; supervised learning; machine learning; modeling; parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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