摘要
This paper investigates the characteristics of a non-stationary time series, which exists in mechanical fault diagnosis. Combining the characteristics with predictive efficiency, the limitation of the ARIMA model prediction method is analyzed. This model often is applied in the prediction of a non-stationary times series in present. Thus, a wavelet prediction method is introduced to solve non-stationary problems. The Mallat method, often used in signal processing, results form the decimation or the retention of one out of every two samples. Its advantage is that just enough information is kept to allow the exact reconstruction of the input series, but the disadvantage is a time-varying series on line cannot be pursued. Therefore, the authors present another method, à Trous method, which can be applied for recursive prediction in real-time sampling procedure.
This paper investigates the characteristics of a non-stationary time series, which exists in mechanical fault diagnosis. Combining the characteristics with predictive efficiency, the limitation of the ARIMA model prediction method is analyzed. This model often is applied in the prediction of a non-stationary times series in present. Thus, a wavelet prediction method is introduced to solve non-stationary problems. The Mallat method, often used in signal processing, results form the decimation or the retention of one out of every two samples. Its advantage is that just enough information is kept to allow the exact reconstruction of the input series, but the disadvantage is a time-varying series on line cannot be pursued. Therefore, the authors present another method, à Trous method, which can be applied for recursive prediction in real-time sampling Procedure.
基金
Sponsored by the National High Technology Research and Development Program of China (Grant No.2002AA721063).
