Optimally Parameterized Wavelet Packet Transform for Machine Residual Life Prediction
نویسندگان
چکیده
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.
منابع مشابه
Wavelet Transform-based Support Vector Machine Model for the Prediction of Residual Settlement in Old Goaf
Multiresolution analyses based on wavelets and support vector machine were combined to establish a wavelet transform-based support vector machine (WT-SVM) model for the prediction of residual settlement in an old goaf. The stochastic volatility of the residual settlement in an old goaf is considered, and the test data of 3 monitoring point in an old goaf in Yanzhou are used. The results are com...
متن کاملUsing Wavelet Support Vector Machine for Fault Diagnosis of Gearboxes
Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the...
متن کاملAn Empirical Study for the Estimation of Autoregressive Hilbertian Processes by Wavelet Packet Method
Abstract. In this paper wavelet packet bases are used for an estimation of the autoregressive Hilbertian processes operator. We assume that integral operator kernel can have some singular structures and estimate them by projecting functional processes on suitable bases. Linear methods for continuous-time prediction using Hilbert-valued autoregressive processes are compared with the suggested me...
متن کاملOn the Use of Wavelets Packet Decomposition for Time Series Prediction
In this paper, we propose Wavelet packet transform based prediction of trends in nonlinear financial time series data. Bombay stock Exchange (INDIA) was selected as a tool to show the Wavelet packet transform based prediction of trends in financial time series. The experimental results demonstrate that the proposed method substantially outperform existing approaches.
متن کاملA New Algorithm for Voice Activity Detection Based on Wavelet Packets (RESEARCH NOTE)
Speech constitutes much of the communicated information; most other perceived audio signals do not carry nearly as much information. Indeed, much of the non-speech signals maybe classified as ‘noise’ in human communication. The process of separating conversational speech and noise is termed voice activity detection (VAD). This paper describes a new approach to VAD which is based on the Wavelet ...
متن کامل