Detection of inter-turn short circuits in induction motors using orthogonal matching pursuit and dictionary learning

Fault detection in induction motors is critical due to their extensive use in industrial applications. Among the various types of faults, stator faults are the most frequent and complex, making early detection particularly challenging. In this paper, a novel methodology for detecting inter-turn short circuits (ITSCs) through stator current analysis is presented. The methodology employs a sine–cosine filter to suppress the fundamental-frequency component, constructs a cumulative distribution function (CDF) to enhance ITSC-related features, and detects faults via a sparse representation of the CDF using the Orthogonal Matching Pursuit algorithm. To verify the methodology's effectiveness, the current stator signals have been analyzed across five levels of fault and four mechanical load conditions. Finally, experimental results show that the proposed method achieves a fault-detection accuracy of 98%, requires a small training dataset, and enables the detection of up to 10 short-circuited turns.