Induction Motor Diagnosis with Broken Rotor Bar Faults Using DWT Technique

Abstract

Vibration signals are widely used in the detection and monitoring of broken rotor bar (BRB) faults. These signals are generally noisy by other sources, which can therefore lead to a loss of information on BRB fault. This paper proposes a denoising method in order to improve the statistical factor sensitivity (correlation coefficient: CC) and the spectral envelope for the early detection of failure of rotor bars. The proposed method is based on a DWT decomposition using the sliding window (db27) associated with an optimized thresholding operation. First, the DWT is applied to the vibration signals to get the approximations and details. Second, every detail is reconstructed, in order to denoise every reconstructed detail. For the exact choice of reconstructed and denoised detail (recd), a statistical study based on the calculation of the correlation coefficient of each reed is carried out. This coefficient is compared to the threshold coefficient. This condition is met in this paper by recd3 and recd4. A spectral envelope of drecd3 and drecd4 is then applied to detect the harmonics, which characterize BRB faults.