著者

松本 心 顔 玉玲 金城 寛 山本 哲彦

出版者

一般社団法人日本機械学会

雑誌

機械材料・材料加工技術講演会講演論文集

巻号頁・発行日

vol.2001, no.9, pp.355-356, 2001-11-02

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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 transform (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 different 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.