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In order to make trend analysis and predic tion to acquisition data in a mechanical equipment condition monitoring system, a new method of trend feature extraction and prediction of acquisition data is p roposed which constructs an adaptive wavelet on the acquisition data by means of second generation wavelet transform (SGWT). Firstly, taking the vanishing momen t number of the predictor as a constraint, the linear predictor and updater are designed according to the acquisition data by using symmetrical interpolating sc heme. Then the trend of the data is obtained through doing SGWT decomposition, t hreshold processing and SGWT reconstruction. Secondly, under the constraint of t he vanishing moment number of the predictor, another predictor based on the acqu isition data is devised to predict the future trend of the data using a non-sym metrical interpolating scheme. A one-step prediction algorithm is presented to predict the future evolution trend with historical data. The proposed method obt ained a desirable effect in peak-to-peak value trend analysis for a machine se t in an oil refinery.
In order to make trend analysis and prediction to acquisition data in a mechanical equipment condition monitoring system, a new method of trend feature extraction and prediction of acquisition data is p roposed which constructs an adaptive wavelet on the acquisition data by means of second generation wavelet Firstly, taking the vanishing momen t number of the predictor as a constraint, the linear predictor and updater are designed according to the acquisition data by using symmetrical interpolating sc heme. Then the trend of the data is obtained through doing doing SGWT decomposition, threshold processing and SGWT reconstruction. Secondly, under the constraint of t vanishing moment number of the predictor, another predictor based on the acquitionition data is devised to predict the future trend of the data using a non-sym metrical interpolation scheme . A one-step prediction algorithm is presented to predict the future evolution trend with historical data. The propose d method obt ained a desirable effect in peak-to-peak value trend analysis for a machine se t in an oil refinery.