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Yayın A DBN based reactive maintenance model for a complex system in thermal power plants(Elsevier Sci Ltd, 2019-10) Özgür Ünlüakın, Demet; Türkali, Busenur; Karacaörenli, Ayşe; Aksezer, Sezgin ÇağlarThermal power plants consist of several complex systems having many interacting hidden components. Any unexpected failure may lead to prolonged downtime and serious lost profits. Therefore, implementing an effective maintenance policy is crucial for this sector. Although preventive maintenance has become a more popular strategy, it does not completely prevent the need for corrective maintenance. Our aim in this study is to tackle the corrective maintenance implementation problem of a multi-component partially observable dynamic system based on a regenerative air heater in a thermal power plant. We propose eight methods having different efficiency measures with respect to time, effect and probability criteria to minimize the total number of maintenance activities in a given planning horizon. Performances of these methods are evaluated under corrective maintenance strategy using dynamic Bayesian networks. The results show that fault effect methods with best working state probability measure perform better than the others considering both the total amount of maintenance activities and also the solution time. We also point out how the methods can be implemented in real-life and how the results can be used for requirements planning. Furthermore, the proposed methods can be used for the corrective maintenance of all systems having hidden interacting components.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 An effective maintenance policy for a multi-component dynamic system using factored POMDPs(Springer Verlag, 2019-09-20) Kıvanç, İpek; Özgür Ünlüakın, DemetWith the latest advances in technology, almost all systems are getting substantially more uncertain and complex. Since increased complexity costs more, it is challenging to cope with this situation. Maintenance optimization plays a critical role in ensuring effective decision-making on the correct maintenance actions in multi-component systems. A Partially Observable Markov Decision Process (POMDP) is an appropriate framework for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems. This study aims to formulate and solve a factored POMDP model to tackle the problems that arise with maintenance planning of multi-component systems. An empirical model consisting of four partially observable components deteriorating in time is constructed. We resort to Symbolic Perseus solver, which includes an adapted variant of the point-based value iteration algorithm, to solve the empirical model. The obtained maintenance policy is simulated on the empirical model in a finite horizon for many replications and the results are compared to the other predefined maintenance policies. Drawing upon the policy results of the factored representation, we present how factored POMDPs offer an effective maintenance policy for the multi-component systems.Yayın Analysis of different maintenance policies on a multi-component system using dynamic bayesian networks(Işık Üniversitesi, 2019-01-15) Karacaörenli, Ayşe; Özgür Ünlüakın, Demet; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans ProgramıRecently, system components and interactions between them have become more complex and this situation has made it di?cult to provide maintenance decisions. Herewith, determining e?ective decisions has played an important role. In multicomponent systems, many methodologies and strategies can be applied when a component or a system has already broken down or when it is desired to identify and avoid pro-actively defects that could lead to future failure. In dynamic systems, it is important for proactive maintenance to increase system reliability by performing early diagnosis-based maintenance activities without waiting for a problem. In this study, we focus on proactive maintenance of a complex multi-component dynamic system. Components are hidden although there exists partial observability to the decision maker. Components deteriorate in time. It is possible to replace or repair components with a given cost. We want to ?nd a policy that minimizes the total maintenance cost in a prede?ned time horizon. We propose several maintenance policies and compare the performance of these by simulating them via Dynamic Bayesian Networks on an empirical model. Furthermore, a dynamic Bayesian network is constructed for the maintenance of an endo generator system to show how the proposed methods can be implemented in real life.Yayın Maintenance policy simulation for a factored partially observable system(The Society for Modeling and Simulation International, 2019-07) Özgür Ünlüakın, Demet; Kıvanç, İpekTaking maintenance decisions is one of the well-known stochastic sequential decision problems under uncertainty. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems of multi-component systems because their state spaces grow exponentially with the increasing number of components. Factored representations have been proposed for POMDPs taking advantage of the factored structure already available in the nature of the problem. Our aim in this study is to show how to formulate a factored POMDP model for the maintenance problem of a multi-component dynamic system and how to simulate and evaluate the obtained policy before implementing it in real life. The sensitivity of the methodology is analyzed under several cost values, and the methodology is compared to other predefined policies. The results show that the policies generated via the POMDP solver perform better than the predefined policies.Yayın Performance analysis of an aggregation and disaggregation solution procedure to obtain a maintenance plan for a partially observable multi-component system(Elsevier Sci Ltd, 2017-11) Özgür Ünlüakın, Demet; Bilgiç, TanerWe analyze the performance of an aggregation and disaggregation procedure in giving the optimal maintenance decisions for a multi-component system under partial observations in a finite horizon. The components deteriorate in time and their states are hidden to the decision maker. Nevertheless, it is possible to observe signals about the system status and to replace components in each period. The aim is to find a cost effective replacement plan for the components in a given time horizon. The problem is formulated as a partially observable Markov decision process (POMDP). We aggregate states and actions in order to reduce the problem space and obtain an optimal aggregate policy which we disaggregate by simulating it using dynamic Bayesian networks (DBN). The procedure is statistically compared to an approximate POMDP solver that uses the full state space information. Cases where aggregation performs relatively better are isolated and it is shown that k-out-of-n systems belong to this class.Yayın Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs(Elsevier Ltd, 2021-07) Özgür Ünlüakın, Demet; Türkali, BusenurIn complex systems with stochastically dependent components which are not observed directly, determining an effective maintenance policy is a difficult task. In this paper, a dynamic Bayesian network based maintenance decision framework is proposed to evaluate proactive maintenance policies for such systems. Two preventive and one predictive maintenance strategies from a cost perspective are designed for multi-component dependable systems which aim to reduce maintenance cost while increasing system reliability at the same time. Tabu procedure is employed to avoid repetitive similar actions. The performances of the policies are compared with a reactive maintenance strategy and also with each other using different strategy parameters on a real life system confronted in thermal power plants for six different scenarios. The scenarios are designed considering different structures of system dependability and reactive cost. The results show that the threshold based maintenance which is the predictive strategy gives the minimum cost and maintenance number in almost all scenarios.Yayın Opportunistic maintenance of complex systems using Dynamic Bayesian Networks(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024-11-04) Demiral, Şimal Ekin; Özgür Ünlüakın, Demet; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans Programı; Işık University, School of Graduate Studies, The Master’s Program in Industrial Engineering – Operations ResearchWith the development of industry, the complexity of systems has increased even more. Unexpected downtimes can create huge costs for companies. Therefore, choosing the appropriate maintenance strategy has become much more important to prevent profit losses. The components in the system and their dependencies can create difficulties in evaluating these strategies. Therefore, it is very important to model the system correctly for the maintenance to be applied to be effective. Bayesian networks (BNs) are highly effective in modeling complex systems and the dependencies between their components. Dynamic Bayesian Networks (DBNs) are the extended versions of BNs by adding a temporal dimension to BNs to represent the dynamic nature of the work. In this study, we presented an opportunistic maintenance framework using DBNs for CNC machines in a production facility. A CNC machine is a partially observable system that consists of many components and has dependencies between its components. We examined different CNC machines and chose the most complex one to model the maintenance problem. We divided the system into ten subsystems, and we chose the Axis System, which was the most functionally important of these subsystems, to study the maintenance problem. We identified cause-and-effect relations in the system using HAZOP analysis. Then, we used DBNs to model the aging of components and the causal relations in the system, and to calculate the probabilistic inferences. We proposed an opportunistic maintenance approach under both corrective and proactive maintenance strategies for this complex system of eleven components with stochastic dependencies among them. We developed two opportunistic maintenance policies to be used in both corrective and proactive maintenance conditions. We tackled the maintenance problem with two different objectives. One is to minimize the total cost while the other is to minimize the total downtime duration. We ran the methods with different parameters in the corrective and proactive maintenance strategies. We compared these two methods with another maintenance methodology that did not use an opportunistic approach. Finally, we determined the conditions where the proposed opportunistic maintenance strategies operated better. The results revealed that opportunistic approaches showed promising performance insituations where a planned or unplanned breakdown led to high downtime costs. Therefore, since the downtime cost in corrective maintenance was higher than that of proactive maintenance, opportunistic policies gave better results in these cases. When the downtime cost in proactive maintenance was increased, the performance of opportunistic policies in proactive maintenance also improved. These findings were also supported by the results of the scenario analysis.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.
