Study On Fault Diagnosis Of Asynchronous Motors Based On Wavelet Analysis

With the development of modern science and technology, and the improvement of electric degree, electric machines play a more and more important role in modern industrial plants. When it works out of order or off-the-line, not only the motor itself is destroyed, but also the whole system would be in danger and the enormous economic losses would be caused. The risk of machine failing can be remarkably reduced if normal service conditions can be arranged in advance. In other words, one may avoid very costly expensive downtime of plant by proper time scheduling of machine replacement or repair if warning of impeding failure can be obtained in advance.As the fault signals of electric machine are non-stationarytransient ones, the traditional signal analysis methods, such as Fourier Transform, are not so efficient and useful for the fault signal extraction. However, Wavelet Analysis has the excellent time-frequency local performance, it can detect the different frequency components of the fault signals by its adjustable time-frequency window. Considering the superiority of Wavelet Transform to non-stationary signals, this paper focuses on how to analyze and extract the fault signals by Wavelet Analysis.The signal analysis theories such as Fourier Transform and Wavelet Transform, are stated in this thesis. The non-stationary and stationary signals of the motor are de-noised by FFT algorithm, Wavelet Transform, Mallat algorithm and Wavelet Packet Transform. As Wavelet Packet Transform has transfer feature to useful signals and restraint feature to noise, just as a band-pass filter, it is most suitable for removing the noise from non-stationary and stationary signals, which is proved effectively by the theory analysis and the comparison results.Wavelet Packet Transform has time-frequency feature and multi-resolution feature. Not only the whole signal but the partialsignal can be analyzed, so fault feature of non-stationary transient signals can be caught correctly. As the vibration signals and current signals of the motor can reflect the early fault, fault diagnosis of the motor can be achieved by the vibration and current signal analysis based on the Wavelet Packet Transform.In this thesis the mechanism of the motor and fault feature frequency of the vibration signals and the current signals are summarized. The feature extraction of the vibration and current signals is processed based on Wavelet Packet analysis. In order to simulate true fault, the fault of the motor is made by man. And in the corresponding conditions the vibration and current signals are sampled. After that the normal and fault sampled signals are analyzed and compared. The Wavelet Packet decomposition and reconstruction of the normal and fault signals are then processed. The characteristic frequency branches are extracted from the reconstruction signals. Through the comparison of the normal and fault characteristic frequency branches, motor fault is identified. In this thesis the fault is diagnosed and the fault feature is extracted by the changes of the feature frequency components. Through thequantitative comparison results of the energy eigenvalue, great differences are found between normal and fault signals. Furthermore, fault diagnosis of asynchronous motors can be achieved correctly based on Wavelet analysis is proved.