Abstract
Despite their ruggedness and reliability, induction motors experience faults due to stresses and manufacturing errors. Early detection of these faults is important in preventing further damages and minimising down-time. In this study, a machine learning algorithm is proposed for detection and classification of broken rotor bar (BRB) faults according to their severity. Removal of high frequency components then amplification was performed on the measured single-phase current. Features were then extracted using FFT and principal component analysis (PCA). Support vector machines (SVM) was used for classification. Two classification schemes were analysed; one classifying in one step and another in two steps. Experiments were performed to evaluate the algorithms by analysing their recognition rates. Six different SVM kernels were studied. Recognition rates as high as 97.9% were achieved. False negative rate as low as 0% was also realised. Furthermore, it was found out that using more principle components does not yield significant improvements.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Intelligent Engineering Informatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
