Broken bar detection in induction motor via wavelet transformation

This paper presents a novel approach for the detection of broken rotor bars in induction motors based on the wavelet transformation. Multiresolution signal decomposition based on wavelet transform or wavelet packet provides a set of decomposed signals in independent frequency bands, which contain independent dynamic information due to the orthogonality of wavelet functions. Also wavelet transforms and wavelet packets in tandem with some signal processing methods, such as autoregressive spectrum, energy monitoring, fractal dimension, etc., can produce desirable results for the condition monitoring and fault diagnosis of induction motors. In the proposed method, broken bar detection is based on the decomposition of the stator currents, where wavelet coefficients of these signals have been extracted. Comparing these extracted coefficients is used for diagnosing the healthy machine from faulty machine. Experimental results are presented for healthy machines and machines with two, three, four and five broken bars in their rotor. Deviation of wavelet coefficients in healthy mode from faulty modes depicts the number of broken bars in the rotor cage. Simulation and experimental results show the effectiveness of the proposed method for broken rotor bar detection in squirrel cage induction motors.