Detection and classification of air gap eccentricity fault in Induction machine using artificial intelligence techniques
1
Electrical Engineering Department, Electrical Engineering Laboratory of Biskra (LGEB) University of Biskra, P.O Box 145, 07000, Biskra, Algeria
2
Electrical Engineering Department, Laboratory of Automation and Signals of Annaba (LASA) Badji Mokhtar-Annaba University,P.O Box.12, Annaba, 23000 Algeria
Submission date: 2024-01-19
Final revision date: 2024-08-04
Acceptance date: 2024-10-24
Online publication date: 2024-10-28
Publication date: 2024-10-28
Corresponding author
Moutaz Bellah Bentrad
Electrical Engineering Department, Electrical Engineering Laboratory of Biskra (LGEB) University of Biskra, P.O Box 145, 07000, Biskra, Algeria
Electrical Engineering Department, Electrical Engineering Laboratory of Biskra (LGEB) University of Biskra, P.O Box 145, 07000, Biskra, Algeria
KEYWORDS
TOPICS
ABSTRACT
This paper employs artificial intelligence to diagnose induction machine health by detecting air gap eccentricity under varied conditions. It addresses Model-Based Method and conventional MCSA techniques limitations, requiring extensive model knowledge. The proposed technique relies on stator current signals, simplifying data acquisition. Using Root Mean Square and raw data using the three phases of stator current from a multi winding model of a squirrel cage induction machine. The study emphasizes on employing classification and regression tasks for supervised learning as a non-model-based approach by applying several models and classifiers to choose the best one for the monitoring task. This approach allows online diagnosis, detecting defects early, even under weak load conditions by conducting a multiclassification technique for each class of the dataset. The paper's strength lies in its holistic analysis of signal fluctuations, categorizing faults based on nature and location. Overall, the proposed algorithm for the classification which is Decision Trees achieved an overall accuracy surpassing 80% against other classifiers, and for the regression task Random Forest outperformed by delivering the least values of loss error with 0.014 using mean square error evaluation metric and achieving a 98.6% accuracy.
FUNDING
This research received no external funding
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