Assessment of the optimal preventive maintenance period using stochastic hybrid modelling
Also the maintenance world is heading towards the era of artificial intelligence applied to the evaluation of the residual life of devices and predictive maintenance. On the other hand, when the operating conditions can change significantly and randomly, influencing the performance of the system sub...
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| Published in | Procedia computer science Vol. 200; pp. 1664 - 1673 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2022.01.367 |
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| Abstract | Also the maintenance world is heading towards the era of artificial intelligence applied to the evaluation of the residual life of devices and predictive maintenance. On the other hand, when the operating conditions can change significantly and randomly, influencing the performance of the system subject to aging phenomena, then the use of a stochastic hybrid approach for the reliability modeling of the system can lead to a closer representation of the reality. This latter approach is applied to the evaluation of the costs associated with the preventive maintenance of the bearings of a centrifugal pump used in petroleum processes. The evaluation is obtained by means of the Monte Carlo simulation methodology. |
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| AbstractList | Also the maintenance world is heading towards the era of artificial intelligence applied to the evaluation of the residual life of devices and predictive maintenance. On the other hand, when the operating conditions can change significantly and randomly, influencing the performance of the system subject to aging phenomena, then the use of a stochastic hybrid approach for the reliability modeling of the system can lead to a closer representation of the reality. This latter approach is applied to the evaluation of the costs associated with the preventive maintenance of the bearings of a centrifugal pump used in petroleum processes. The evaluation is obtained by means of the Monte Carlo simulation methodology. |
| Author | Chiacchio, F. Compagno, L. D’Urso, D. Sinatra, A. |
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| Cites_doi | 10.1016/j.procs.2021.01.262 10.1115/DETC2014-34326 10.1016/j.ress.2020.106904 10.1080/07408170208928880 10.3390/app11052300 10.1108/JQME-02-2017-0008 10.1016/j.anucene.2019.107139 10.1080/24725854.2018.1437301 10.1080/03610918.2016.1183781 10.1109/ACCESS.2021.3067478 10.1016/j.cie.2019.106024 10.1016/j.ress.2018.03.022 10.1016/j.ress.2016.11.011 10.1177/1748006X14533958 10.1016/j.energy.2018.03.101 10.3390/info10090283 10.1016/j.eswa.2015.10.046 10.1016/j.jsp.2009.10.001 10.1016/j.renene.2020.06.142 10.1016/S1474-6670(17)32784-2 |
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| Keywords | Preventive Maintenance Bearings Monte Carlo Simulation Stochastic Hybrid Automation |
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| Title | Assessment of the optimal preventive maintenance period using stochastic hybrid modelling |
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