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Yayın Cost-effective fault diagnosis of a multi-component dynamic system under corrective maintenance(Elsevier Ltd, 2021-04) Özgür Ünlüakın, Demet; Türkali, Busenur; Aksezer, Sezgin ÇağlarMaintenance 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.Yayın Medical device maintenance strategy for post pandemic: case on ventilators(IEOM Society International, 2022-06) Aksezer, Sezgin ÇağlarEquipment-demand of healthcare providing institutions increased drastically during the Covid-19 pandemic. While majority of the demand (also the most publicized) has been occurring for disposable products such as masks, gloves, gowns, sterilizers, and test kits; the demand for electronic devices also increased during the initial waves of the pandemic but dropped afterwards as it was satisfied by the manufacturers. Since devices like vital monitors, dialysis systems, and ventilators are not single use products, handling of such systems after acquisition has become an issue of operations management for healthcare professionals. Utilization of such devices seem to have a cyclic behavior correlated with pandemic waves (as the hospitalizations peak so the usage and vice versa) and tend to have a longterm decreasing trend as the Covid-19 pandemic transforms to epidemic. Ventilators have found extensive usage in the treatment of Covid-19 related patients for ICU and emergency admissions. Medical ventilator device provides mechanically simulated air into the lungs of patients who are completely unable to breathe by themselves or breathes but insufficient to carry the necessary oxygen. So, the total functionality is crucial and service readiness is imminent. Number of active ventilators in Turkey increased to 22.000 units by the first year of the pandemic. However, utilization ratio of ventilators dropped to 29.6% (as of March 17, 2022) from the heights of 55% observed in the winter of 2021. Because of such decline, devices have become idle or operational times have dropped significantly. Health institution under investigation is a full-service government hospital operating in the city of Istanbul, Turkey. There are currently 142 ventilators in the hospital with less than 35% utilization. Maintenance task is only carried out by manufacturer approved service providers and parts are supplied from abroad. Since the operator of devices is a non-profit government hospital, maintenance outsourcing is done by bidding with strict budget and usually under the pressure of currency exchange rates even after the maintenance contract is signed. Failure analyses indicate mean time to failure of devices has not changed significantly with respect to pre-pandemic operation. Periodic maintenance strategy that has long been adopted for such devices has been modified to consider cyclic operation, extended standby durations, and lead time of spare parts. Further proposals are under consideration for group maintenance involving multiple healthcare institutions.Yayın A DBN based prognosis model for a complex dynamic system: a case study in a thermal power plant(Springer Nature Switzerland AG, 2018-08-15) Özgür Ünlüakın, Demet; Kıvanç, İpek; Türkali, Busenur; Aksezer, Sezgin ÇağlarWith the development of industry, complexity of systems and equipment has increased extensively. This results in the introduction of many interdependencies (stochastic, structural and economic) among the components of systems. Neglecting these interdependencies, when planning maintenance actions, leads to undesirable outcomes such as prolonged downtime and higher costs. That is why a multi-component system approach needs to be taken into account in maintenance planning models. However, maintenance planning is a difficult task in multi-component systems because of their complexities. Energy production systems are notable examples of such complex structures consisting of many interacting components. Maintenance planning is extremely crucial for this sector since any unexpected malfunction leads to very serious costs. Therefore, the aim of this study is to formulate the maintenance problem of a multi-component dynamic system in thermal power plants focusing on system reliability prognosis. Bayesian networks (BN) are probabilistic graphical models that have been extensively used to represent and model the causal relations. A dynamic Bayesian network (DBN) is an extended BN which has a temporal dimension. We propose to use DBNs to prognose the reliabilities of components and processes of a dynamic system in a thermal power plant and show that this representation is efficient to model the interdependencies and degradations in such a system.Yayın Analyzing the performance of different costBased methods for the corrective maintenance of a system in thermal power plants(World Academy of Science, Engineering and Technology (WASET), 2019-08-16) Özgür Ünlüakın, Demet; Türkali Özbek, Busenur; Aksezer, Sezgin ÇağlarSince the age of industrialization, maintenance has always been a very crucial element for all kinds of factories and plants. With today’s increasingly developing technology, the system structure of such facilities has become more complicated, and even a small operational disruption may return huge losses in profits for the companies. In order to reduce these costs, effective maintenance planning is crucial, but at the same time, it is a difficult task because of the complexity of systems. The most important aspect of correct maintenance planning is to understand the structure of the system, not to ignore the dependencies among the components and as a result, to model the system correctly. In this way, it will be better to understand which component improves the system more when it is maintained. Undoubtedly, proactive maintenance at a scheduled time reduces costs because the scheduled maintenance prohibits high losses in profits. But the necessity of corrective maintenance, which directly affects the situation of the system and provides direct intervention when the system fails, should not be ignored. When a fault occurs in the system, if the problem is not solved immediately and proactive maintenance time is awaited, this may result in increased costs. This study proposes various maintenance methods with different efficiency measures under corrective maintenance strategy on a subsystem of a thermal power plant. To model the dependencies between the components, dynamic Bayesian Network approach is employed. The proposed maintenance methods aim to minimize the total maintenance cost in a planning horizon, as well as to find the most appropriate component to be attacked on, which improves the system reliability utmost. Performances of the methods are compared under corrective maintenance strategy. Furthermore, sensitivity analysis is also applied under different cost values. Results show that all fault effect methods perform better than the replacement effect methods and this conclusion is also valid under different downtime cost values.
