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    Proactive maintenance of thermal power plants under limited observations
    (Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2013-06) Abushanap, Mo’Tasem; Özgür Ünlüakın, Demet
    In the past few decades, because of the technological revolution, interactions between the components of a system become more sophisticated which makes the maintenance decision making procedure a hard issue. At this point, effective problem diagnosis plays an important role in determining the right maintenance decisions. The objective of diagnosis is to analyze and determine the most likely causes of a problem. Hence the data or observations gathered so far helps the decision maker in effective diagnosis. On the other hand, monitoring and predicting system health is also important especially for dynamic systems where proactive maintenance is preferred instead of the reactive one. Improving system reliability by performing maintenance activities based on early diagnosis before a serious problem arises is essential in proactive maintenance. In this study, we consider the proactive maintenance decisions of thermal power plants consisting of interacting components under limited observations over a planning horizon. Maintenance activities are performed at any time by replacing either aging components or gauges in the system. The objective is to determine an optimum proactive maintenance plan in a discrete planning horizon. We use dynamic Bayesian networks (DBNs) for representation and to do fast inference. We propose two proactive maintenance methodologies and present their predicted maintenance plans. At any time when a replacement decision is made, three different diagnosis techniques, where two of them are from literature, are used. Computational analyses show that there exists no significance difference among the performances of these methods. The proposed methodologies can be easily adapted for proactive maintenance planning of other complex dynamic systems.