Method Of EMD And ZOOM-FFT To Detect The Broken Bars Fault Of Induction Motor

Induction motors are widely used in the industrial and agricultural production because of its simple structure, low price, high reliability and convenient maintenance. With the rapid development of the modern industrial system, the capacity of a single motor is keeping increasing and the load is also becoming more complicated now. A motor failure not only can result in damage to the motor, but also unscheduled machine downtime and the shutdown of a production line, which will cause heavy financial losses and catastrophic failure. Statistical studies have show that the rotor broken bar fault, which accounts for nearly 10% of total induction motor failures, is the most familiar fault for induction motors. Induction motor rotor bar fault detection has become one of the most challenging issues.It is well known that broken bars can be detected by analyzing the stator current spectrum, when broken bars occur. Broken rotor bars in induction motor cause the presence of sidebands at frequency (1-2s)f1 around the main frequency component in the stator current spectrum, where f1 is the supply frequency and s is the slip. But the sideband component is much closed to the main frequency f1, and its amplitude is very small. Although the fast Fourier transform (FFT) is an effective method and widely used in signal processing, it is suitable for stationary signal processing and some time domain information may be lost. Recently different diagnostic techniques, which is superior to the FFT spectrum analyze method for non-stationary signal, have been developed to identify rotor bar faults, most of which are strongly dependent on detecting the twice slip frequency modulation in stator current, such as time-frequency analysis, wavelet and wavelet packet transformation, Hilbert transformation, et al.The principle and features of the rotor broken bars in induction motor, especially the signal processing methods in failure diagnosis and the analysis of the traditional stator current Fourier transform data processing are presented in this paper. Hilbert-Huang transformation is a new type of signal processing methods, which is ideal for handling non-linear, non-stationary signals. This approach is an adaptive signal processing methods, which includes two processes: EMD (Empirical Mode Decomposition) which is the most crucial part and Hilbert transformation. ZOOM-FFT is a refinement of the election with Fourier Transform, has a high frequency resolution and is conducive to small differences in the frequency. Therefore, according the signal characteristics of broken-bar induction motors, the method of EMD and ZOOM-FFT is presented in this paper to deal with the stator current sampling signal. The stator current is firstly decomposed with EMD, then the IMF (intrinsic mode function) component which contains the broken bar fault feature is spectrum analyzed by using ZOOM-FFT. The proposed method is demonstrated efficaciously by the stator current analysis result from the simulation and actual measurement.