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• W2182012906 abstract "One of the prevalent issues in condition based maintenance (CBM) is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called Residual life prediction based on optimally parameterized Wavelet transform and Mute-step Support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications and decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature-signal energy contents. The prediction model is based on multi-step support vector regression (MSVR) and prediction accuracy is improved in comparison with the techniques based on support vector regression (SVR). Performance of RWMS is evaluated in terms of Root Means Square Error (RMSE), studies show that proposed algorithm predicts the residual life of the equipment accurately." @default.
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• W2182012906 date "2011-01-01" @default.
• W2182012906 modified "2023-09-27" @default.
• W2182012906 title "Optimally Parameterized Wavelet Packet Transform for Machine Residual Life Prediction" @default.
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