@inproceedings{scholars11242,
            year = {2019},
             doi = {10.1109/SCORED.2019.8896330},
            note = {cited By 5; Conference of 17th IEEE Student Conference on Research and Development, SCOReD 2019 ; Conference Date: 15 October 2019 Through 17 October 2019; Conference Code:154444},
           pages = {11--16},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2019 IEEE Student Conference on Research and Development, SCOReD 2019},
           title = {Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization},
            isbn = {9781728126135},
          author = {Rosli, N. S. and Ain Burhani, N. R. and Ibrahim, R.},
        abstract = {Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE) {\^A}{\copyright} 2019 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075635449&doi=10.1109\%2fSCORED.2019.8896330&partnerID=40&md5=57026a08d8a8b4317fe0bd50c8f3153b},
        keywords = {Gas compressors; Maintenance; Mean square error; Neural networks, Booster compressor; High demand; High impact; Motor failure; Optimal weight; Predictive maintenance; Root mean square errors, Particle swarm optimization (PSO)}
}