| Issue |
E3S Web Conf.
Volume 336, 2022
The International Conference on Energy and Green Computing (ICEGC’2021)
|
|
|---|---|---|
| Article Number | 00051 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202233600051 | |
| Published online | 17 January 2022 | |
Supervised Classification Of induction Motors faults
1 National School of Applied Sciences, Abdelmalek Essaadi University, Tangier Morocco
2 Institut de Thermique, Mécanique, et Matériaux (ITheMM EA7548) -UFR Sciences Reims France.
* Corresponding author: elbouisfi.radouane@gmail.com
Currently, the environmental challenges have been considered as a strategic issue for most industrial companies around the world, threatening their sustainability and profit; This leads to taking the environmental dimensions seriously and preserving natural resources well, since they are a key criterion for sustainable development. In this context, this work calls for innovative solution and new technologies to support the development and integration of environmental considerations through the implementation of an automated fault detection and diagnosis system in induction machines in order to minimize downtime, increase machine utilization rate, get an idea of remaining machine life based on artificial intelligence (AI) and the analysis of collected data. Using the Pattern Recognition methods, this system aims to support decision making in terms of defect classification, through the following process: the collection of relevant data about the stator currents of two induction machines, powered by a converter, one healthy and the other defective, through the CompactRIO device, then the analysis of the data, using programs developed under LabVIEW software, and the extraction of the indicators to form a database. Based on analysis results, several intelligent methods by classification algorithms can organize the acquired data in order to automate the diagnostic process. Ultimately, the set-up of an alert system to prevent rather than cure. The outcomes showed that the integration of predictive maintenance could help achieve an energy cost recovery equal to10% of the total costs of an electric motor system. Hence, the premature detection of faults helps to minimize energy expenditure and achieve overall cost savings, which implies energy optimization.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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