Machine Fault Diagnosis Using a Neural Network Based on Autocorrelation Coefficients of Wavelet Transformed Signals
Machine Fault Diagnosis Using a Neural Network Based on Autocorrelation Coefficients of Wavelet Transformed Signals Kokoro Matsumoto, Non-member (Okinawa Hitachi Network Systems Co., Ltd.), Yuling Yan, Non-member (University of Wisconsin), Hiroshi Kinjo, Member, Tetsuhiko Yamamoto, Non-member (University of the Ryukyus) In this paper, a diagnosis method for machine faults using a neural network based on autocorrelation coefficients of wavelet transformed signals is presented. It is important for factory engineers to accurately estimate machine faults. In conventional diagnosis methods, frequency analysis using the fast Fourier trans form (FFT) has often been employed. Recently, wavelet transforms have been studied and applied to many signal-processing applications. Wavelet transforms are very useful because of characteristics of time-frequency analysis. In this paper, we propose an application of wavelet transforms to machine fault diagnosis. In order to apply wavelet transforms to machine fault diagnosis, we use autocorrelation coefficients of the wavelet transformed signal. In this research, it becomes clear that the autocorrelation coefficients represent the dif ferent classes of machine states. For the automatic diagnosis, we trained a neural network to recognize three classes of machine states based on the autocorrelation coefficients of wavelet transformed signals. Simulation and experimental results show that the trained neural network could successfully estimate machine faults.