Abstract
This paper presents a fault diagnosis scheme for induction machines (IMs) using Support Vector Machine (SVM) and Random Forests (RFs). First, a number of time domain and frequency-domain features are extracted from vibration and current signals in different operating conditions of IM. Then, these features are combined and considered as the input of SVM-based classification model. To avoid overfitting, RF is utilized to determine the most dominant features contributing to accurate classification. It is proved that the proposed method is capable of achieving highly accurate fault diagnosis results for broken rotor bar and eccentricity faults and it can appropriately handle the high dimensionality of the combined data.
Original language | English |
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Title of host publication | Proceedings of the International Conference on Electrical Machines, ICEM 2020 |
Publisher | IEEE |
Number of pages | 7 |
ISBN (Electronic) | 9781728199450 |
ISBN (Print) | 978-1-7281-9945-0 |
DOIs | |
Publication status | Published - 2020 |
MoE publication type | A4 Article in a conference publication |
Event | International Conference on Electrical Machines - Virtual, Online Duration: 23 Aug 2020 → 26 Aug 2020 Conference number: 25 |
Publication series
Name | Proceedings (International Conference on Electrical Machines) |
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Publisher | IEEE |
ISSN (Print) | 2381-4802 |
ISSN (Electronic) | 2473-2087 |
Conference
Conference | International Conference on Electrical Machines |
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Abbreviated title | ICEM |
City | Virtual, Online |
Period | 23/08/2020 → 26/08/2020 |
Other | Virtual Conference |
Keywords
- Fault diagnosis
- Induction motors
- Machine learning
- Multible signal classification
- Support vector machine