System identification: theory for the user
System identification: theory for the user
A New Look at Nonlinear Time Series Prediction with NARX Recurrent Neural Network
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
A hybrid model for exchange rate prediction
Decision Support Systems
Methodology for long-term prediction of time series
Neurocomputing
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
WSEAS Transactions on Systems and Control
Fault prognosis of mechanical components using on-line learning neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
ECC'11 Proceedings of the 5th European conference on European computing conference
A recursive model for nonlinear spring-mass-damper estimation of a vehicle localized impact
GEMESED'11 Proceedings of the 4th WSEAS international conference on Energy and development - environment - biomedicine
Computers and Industrial Engineering
Information Sciences: an International Journal
Hi-index | 12.05 |
This paper presents an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) model and autoregressive moving average (ARMA) model for long-term machine state forecasting based on vibration data. In this study, vibration data is considered as a combination of two components which are deterministic data and error. The deterministic component may describe the degradation index of machine, whilst the error component can depict the appearance of uncertain parts. An improved hybrid forecasting model, namely NARX-ARMA model, is carried out to obtain the forecasting results in which NARX network model which is suitable for nonlinear issue is used to forecast the deterministic component and ARMA model are used to predict the error component due to appropriate capability in linear prediction. The final forecasting results are the sum of the results obtained from these single models. The performance of the NARX-ARMA model is then evaluated by using the data of low methane compressor acquired from condition monitoring routine. In order to corroborate the advances of the proposed method, a comparative study of the forecasting results obtained from NARX-ARMA model and traditional models is also carried out. The comparative results show that NARX-ARMA model is outstanding and could be used as a potential tool to machine state forecasting.