A novel approach to 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. A multi-resolution signal decomposition based on wavelet transform or wavelet packet provides a set of decomposed signals in independent frequency bands which contain a large amount of independent dynamic information due to the orthogonality of wavelet functions. Wavelet transform and wavelet packet in tandem with some signal processing methods such as autoregressive spectrum, energy monitoring, fractal dimension, etc., can produce many desirable results for condition monitoring and the fault diagnosis of an induction motor. Broken bar detection is based on decomposing the stator currents, and then extracting the wavelet coefficients of these signals. Comparing these extracted coefficients can diagnose a healthy machine from a faulty machine. Experimental results are presented in healthy, two, three, four, and five broken bars. Deviation of wavelet coefficients in a healthy mode from a faulty mode shows 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