| Autor: |
Hadi Ashraf Raja, Bilal Asad, Toomas Vaimann, Ants Kallaste, Anton Rassolkin, Anouar Belahcen |
| Přispěvatelé: |
Trnka, Pavel, Tallinn University of Technology, Department of Electrical Engineering and Automation, Aalto-yliopisto, Aalto University |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 International Conference on Diagnostics in Electrical Engineering (Diagnostika). |
| DOI: |
10.1109/diagnostika55131.2022.9905174 |
| Popis: |
Publisher Copyright: © 2022 IEEE. With advancements in science, machine learning and artificial intelligence integration with different fields have opened up new horizons. In this paper, some simplified custom machine learning algorithms are defined to train different faults for electrical machines. The industry has been moving towards predictive maintenance of machines rather than scheduled maintenance with the new industry 4.0 revolution. It has also paved the way for researchers to explore more in machine learning and have specific machine learning training algorithms catered to diagnose faults in electrical machines. Here, three different variations of a simplified machine learning algorithm are present for the training of faults of electrical machines. A comparison of the results is presented at the end, along with further studies carried out in this area. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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