Bearing fault detection of induction motor using SWPT and DAG support vector machines
| Autor: | Firas Ben Abid, Ahmed Braham, Slaheddine Zgarni |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Bearing (mechanical) business.industry 020208 electrical & electronic engineering Pattern recognition Control engineering 02 engineering and technology Signature (logic) Fault detection and isolation Wavelet packet decomposition law.invention Support vector machine law Component (UML) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Induction motor |
| Zdroj: | IECON |
| DOI: | 10.1109/iecon.2016.7793237 |
| Popis: | Bearings are considered as a critical component in Induction Motors (IM). An approach based on Motor current Signature analysis (MCSA) is presented to detect bearing faults (BF). This study is subject for a novel pattern recognition approach for BF detection in IM combining Stationary Wavelet Packet Transform (SWPT) and DAG SVM. Four bearing conditions are tested. As results, it is shown that the proposed approach permits to distinguish with full accuracy different bearing conditions regardless the load level. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|