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Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors.
- Source :
-
Energies (19961073) . Aug2024, Vol. 17 Issue 15, p3723. 21p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 17
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 178949173
- Full Text :
- https://doi.org/10.3390/en17153723