Abstract
AbstractWe study maintenance of a complex dynamic system consisting of ageing and unobservable components under a predetermined threshold reliability level. Our aim is to construct an optimum replacement policy for the components of the system by minimizing total number of replacements or total replacement cost. We represent the problem with dynamic Bayesian networks (DBNs). We prove that under the existence of a predetermined threshold reliability, performing replacements at periods when the system reliability just falls below the threshold assures optimum replacement times. Four component selection approaches and their cost focused versions are proposed to choose the component to replace and are tested on a complex dynamic problem. Their performances are analyzed under various threshold and cost levels.
Highlights
Maintenance is becoming an increasingly difficult task with the increasing complexity of the systems
We develop an algorithm for determining replacement policies of a complex dynamic system with unobservable components within the framework of dynamic Bayesian networks (DBNs) representation
We propose to perform replacements at periods when the system reliability just falls below the threshold
Summary
Maintenance is becoming an increasingly difficult task with the increasing complexity of the systems. One can be either proactive or reactive in maintaining a complex system. If the system breaks down and the maintenance (repair) is performed, this is reactive. Proactive maintenance can either be performed on a fixed schedule or it can be adaptively applied.[1] We consider the reliability-centered preventive maintenance activities of a complex dynamic system comprised of ageing components. It is not possible to directly ob-
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