@article{scholars19472,
           pages = {593--609},
         journal = {Lecture Notes in Mechanical Engineering},
           title = {Evaluation of NARX Network Performance on the Maintenance Application of Rotating Machines},
            year = {2023},
             doi = {10.1007/978-981-19-1939-8{$_4$}{$_6$}},
            note = {cited By 1; Conference of 7th International Conference on Production, Energy and Reliability, ICPER 2020 ; Conference Date: 14 July 2020 Through 16 July 2020; Conference Code:284729},
        abstract = {Rotating machines are widely used in many industries even though the maintenance of this equipment is high.Most of the industries employ preventive maintenance to avoid expensive failures and shutdown.From the view of maintenance application, a predictive approach utilizing real time data provide a better understanding of the system degradation.This paper presents the evaluation of ANN technique for a data driven model which is nonlinear autoregressive neural network design with exogenous input (NARX) for a maintenance application for rotating machines.The data used for the research are obtained from NASA Data Depository.Analysis was done to understand the data better.Before model training, a feature selection for the inputs have been made allowing dimensionality reduction narrowing the parameters consisting features that could make an impact on detecting failure.NARX neural network model from the neural net time series is chosen as it can adequately handle a time series data in a forecasting model network.NARX network is first trained in open-loop mode before being trained in closed-loop mode.Results show that the network model is suitable for time series data prediction. {\^A}{\copyright} 2023, Institute of Technology PETRONAS Sdn Bhd.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140788784&doi=10.1007\%2f978-981-19-1939-8\%5f46&partnerID=40&md5=8f3974768806924e080dfe2985f524fe},
        keywords = {NASA; Operating costs; Preventive maintenance; Rotating machinery; Sensitivity analysis; Time series, Autoregressive neural networks; Conditioned-based maintenance; Data-driven model; Exogenous input; NARX network; Neural network designs; Real-time data; Rotating machine; System degradation; Time-series data, Neural networks},
          author = {Samsuri, N. A. and Raman, S. A. and Tuan Ya, T. M. Y. S.}
}