Automatic Fault Diagnosis System for Induction Motors During Transient Conditions

This paper presents a novel system for the fault diagnosis of induction motors, employing the Transient Motor Current Signature Analysis (TMCSA) method. The developed system operates in a laboratory environment and enables the detection of motor faults during transient conditions, specifically during the startup phase. The diagnostic process relies on tracking characteristic patterns in the time–frequency domain, which are extracted from current signals using advanced signal processing techniques, including the Gabor transform, Short-Time Fourier Transform (STFT), Wigner–Ville distribution, and Continuous Wavelet Transform (CWT). These transformations allow precise identification of fault-related components and their evolution over time. Experimental investigations were conducted for two distinct types of faults: a broken rotor bar and mixed eccentricity. The obtained results demonstrate a high accuracy of fault detection and confirm the robustness of the proposed method. Furthermore, the findings indicate its suitability for practical applications in variable-speed drive systems, where conventional steady-state diagnostic methods are often ineffective.