Inter‐Turn Fault Diagnosis of Induction Motor Using a Root‐Prony and Fuzzy Logic Method

This paper proposes a novel diagnostic approach for detecting inter‐turn short‐circuit faults in induction motors, combining the root‐Prony method with fuzzy logic. Traditional techniques, such as the periodogram, have limitations in detecting low‐magnitude harmonics and providing high‐frequency resolution. To address these challenges, complex high‐resolution methods, such as MUSIC and ESPRIT, have been developed. In this study, the root‐Prony method is selected for its adaptability and low computational burden as it does not rely on space decomposition, making it faster than MUSIC. The proposed approach focuses on analysing the stator current signal within a specific frequency range near the fundamental rotor slot harmonics. By reducing the number of processed samples, computation time is further decreased. The integration of fuzzy logic enables intelligent decision‐making regarding the condition of the stator circuit by considering harmonic magnitudes under different load torque values for accurate diagnosis. Experimental tests were conducted on an induction motor initially powered directly from an electrical network supplying symmetrical sinusoidal three‐phase voltages. To demonstrate the robustness of the proposed method in noisy environments, additional tests were performed with the motor powered by a converter. In such scenarios, the conventional periodogram‐based technique was unable to detect the desired harmonics due to the high harmonic content in the stator current signals. The test results confirm the superior effectiveness of the root‐Prony method over the classical periodogram technique in estimating the frequencies and amplitudes of the targeted harmonics. The integration of the root‐Prony method with fuzzy logic offers an advanced, efficient and reliable solution for fault diagnosis in induction motors.