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Farhadi, Fatemeh, Amin-Nasseri, Mohammad Reza. (1395). Prediction of Rotating Machineries Failure by Intelligent Systems. , 50(3), 461-470. doi: 10.22059/jieng.2016.63177
Fatemeh Farhadi; Mohammad Reza Amin-Nasseri. "Prediction of Rotating Machineries Failure by Intelligent Systems". , 50, 3, 1395, 461-470. doi: 10.22059/jieng.2016.63177
Farhadi, Fatemeh, Amin-Nasseri, Mohammad Reza. (1395). 'Prediction of Rotating Machineries Failure by Intelligent Systems', , 50(3), pp. 461-470. doi: 10.22059/jieng.2016.63177
Farhadi, Fatemeh, Amin-Nasseri, Mohammad Reza. Prediction of Rotating Machineries Failure by Intelligent Systems. , 1395; 50(3): 461-470. doi: 10.22059/jieng.2016.63177

Prediction of Rotating Machineries Failure by Intelligent Systems

Advances in Industrial Engineering
مقاله 11، دوره 50، شماره 3، اسفند 2016، صفحه 461-470    اصل مقاله (1.13 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2016.63177
نویسندگان
Fatemeh Farhadi؛ Mohammad Reza Amin-Nasseri*
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
چکیده
Failure of machines, due to stopping the production line, results in financial losses. Preventive maintenance, significantly extends the machineries life, and reduces the costs. On the other hand, predicting the remaining useful life (URL) of the equipment and machineries, provides adequate time for maintenance engineers to repair or replace the parts before failure occurs, and avoid the overhaul costs (conditional-based maintenance). These actions are more important for rotary machines such as turbines, pumps and compressors, than the others. Hence, in this paper, we predict the URL of the Olefin unit of Pars Petrochemical Company turbine pumps based on the bearings health by artificial neural networks (ANN) and support vector machine. First, we provided the prediction model by the RMS, mean, peak and crest factor of one bearing, which was used to estimate the RUL of the four bearings using the above methods. Results showed that the accuracy of prediction by SVM method was more than single-layer ANN.
کلیدواژه‌ها
Artificial Neural Network؛ Bearing؛ prediction؛ Remaining useful life (RUL)؛ Support vector Machine؛ Turbine pump
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