Detection of induction motor broken rotor bar faults under no load condition by using support vector machines. (29th January 2022)

• Record Type: Journal Article
• Authors: Arabacı, Hayri; Mohamed, Mohamed Ali
• Is Part Of: International journal of intelligent engineering informatics. Volume 9:Number 5(2021)
• Page Start: 470
• Page End: 486
• Publication Date: 2022-01-29
• Subjects: current measurement -- fault detection -- feature extraction -- induction motors -- spectral analysis; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

An important fault in induction motor is the broken rotor bar. Many techniques have been proposed for the detection of the rotor fault. However, the traditional techniques like motor current signature analysis have difficulty in detecting the rotor faults at 'no load' condition due to low slip. In this study, an algorithm which uses fast Fourier transform, principal component analysis and intelligent classifiers is proposed. The proposed algorithm was able to accurately detect the rotor faults of different severity levels at low slip. Experiments were carried out with three submersible induction motors. Four different rotor faults and healthy motor conditions were investigated for each motor. The motors were loaded different load levels to test the proposed algorithm. The best results were achieved with medium Gaussian support vector machine. The condition of having any faulted bar in the motor was obtained with 100% accuracy. Faults classification carried out by 92.2% accuracy.