@inproceedings{scholars11600,
         journal = {I2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings},
           title = {Optimized neural network of predictive maintenance for air booster compressor (ABC) motor failure},
            year = {2019},
             doi = {10.1109/I2MTC.2019.8827145},
            note = {cited By 2; Conference of 2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019 ; Conference Date: 20 May 2019 Through 23 May 2019; Conference Code:151873},
          volume = {2019-M},
            isbn = {9781538634608},
          author = {Rosli, N. S. B. and Ibrahim, R. B. and Ismail, I.},
        abstract = {Recently, predictive maintenance of complex machines is becoming a vital issue in industry 4.0. This is because of inexplicably occur in high voltage rotating machine can give prominent impact towards the industrial economy. Therefore, an accurate predictive maintenance is needed in reducing the degree of damage to the industrial instruments. the current method was not sufficient to indicate satisfactory result to large industrial plants. Additional auxiliary techniques are required to develop predictive maintenance. This paper suggests some methods to optimize the model of Neural Network (ANN) using Particle Swarm optimization (PSO) and Spiral Dynamics Algorithm (SDA). The proposed predictive maintenance algorithm has been verified with actual data of ABC Motor. The ANN model was subsequently modified using PSO and SDA to provide an accurate and optimal network architecture with improved accuracy results. The result demonstrated in this paper by comparing the conventional ANN with optimized ANN with the value of Mean Square Error (MSE) and Root Mean Square Error (RMSE). {\^A}{\copyright} 2019 IEEE.},
        keywords = {Gas compressors; Industrial plants; Maintenance; Mean square error; Network architecture; Neural networks, Auxiliary techniques; High voltage rotating machines; Industrial instruments; Neural networks (ANN); Optimal network architecture; Predictive maintenance; Root mean square errors; Spiral dynamics, Particle swarm optimization (PSO)},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072822006&doi=10.1109\%2fI2MTC.2019.8827145&partnerID=40&md5=8a67ef9607e010a4a43b26059f1a7fd4}
}