Cost-effective fault diagnosis of a multi-component dynamic system under corrective maintenance
| Autor: | Demet Özgür-Ünlüakın, S. Çağlar Aksezer, Busenur Türkali |
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| Přispěvatelé: | Işık Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Işık University, Faculty of Engineering, Department of Industrial Engineering, Özgür Ünlüakın, Demet, Türkali, Busenur, Aksezer, Sezgin Çağlar |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Maintenance Computer science Cost effectiveness Corrective actions Maintenance cost 02 engineering and technology Fault (power engineering) Complex structure Knowledge-based systems 020901 industrial engineering & automation Cost benefit analysis Component (UML) 0202 electrical engineering electronic engineering information engineering Efficiency measure Multi-component systems System integrity Stochastic systems Cost–benefit analysis Corrective maintenance State probability Costs Reliability engineering Dynamic Bayesian networks Bayesian networks Maintenance planning Knowledge based systems 020201 artificial intelligence & image processing Sensitivity analysis Software |
| Zdroj: | Applied Soft Computing. 102:107092 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2021.107092 |
| Popis: | Maintenance planning and execution are challenging tasks for every system with complex structure. Interdependent nature of the components that builds up the system may have significant effect on system integrity. While preventive maintenance actions can be carried out in a more planned fashion, corrective actions are more time sensitive as they directly affect the availability of the system. This study proposes a cost-effective dynamic Bayesian network modeling scheme to be used in the planning of corrective maintenance actions on systems having hidden components which have stochastic and structural dependencies. In such context, the regenerative air heater system which is a key element of a power plant is taken into consideration. The proposed maintenance framework offers several methods, each aiming to balance the cost with the probability effect using a normalization procedure. The methodologies are extensively simulated for sensitivity analysis under various downtime cost values. Fault effect methods with worst state probability efficiency measures give the least total cost for all downtime cost values and their distinction becomes significant as this value increases. Further statistical analysis concludes that considerable gains on maintenance costs can be achieved by the proposed approach. This research is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant: 117M587. Publisher's Version |
| Databáze: | OpenAIRE |
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