A preventive replacement policy based on system critical condition

Abstract

The article considers a linear consecutive-k-out-of-n: F system and a linear connected-(r, s)-out-of-(m, n): F system. It is assumed that the components are identical and the failure times of components follow an exponential distribution. The system is continuously monitored and the component failure can be known at any time. A preventive maintenance policy is proposed, and it is based on the system critical condition which is related to the number of working components in the minimal cut sets of the system. If there is at least one minimal cut set which consists of only one working component, the system is maintained preventively after a certain time interval and the failed components are replaced with the new ones to prevent the system failures. The expected cost rate is used as an optimization criterion. The expected cost rate is estimated by simulation and numerical examples are studied. The numerical analyses are exploratory and not generalizable.

State-dependent age replacement policy for a system subject to cascading failures

“…Furthermore, in the literature, various preventive maintenance policies have been developed and applied to prevent sudden unexpected failures of such systems. 14–19 The most common, popular, and thus important maintenance policy might be the ordinary age-dependent preventive maintenance policy. 20 However, when the number of working components in the system can be observed, the ordinary age replacement policy may not be efficient and it would be reasonable to consider a replacement policy which takes into account the state of the system (i.e.…”

A preventive maintenance of circular consecutive-k-out-of-n: F systems

“…Shen and Cui (2015) considered a circular consecutive- k -out-of- n : F system with sparsely connected IID components and developed some equations for system reliability and Birnbaum importance by means of the finite Markov chain imbedding approach. Yun and Endharta (2016) developed a condition-based PM policy for linear consecutive- k -out-of- n : F systems and linear connected-( r , s )-out-of-( m , n ): F systems and used simulation to get the optimal decision variable. Endharta and Yun (2015) used simulation for periodic inspection policy.…”

A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods

“…Usually, the decision makers choose between condition-based maintenance (CBM) policy 8,9 or preventive maintenance (PM) policy. 10 The choice of maintenance policies, then, depends on the decision makers’ attitude to the risk of relying on a predicted RUL to plan maintenance services. Undoubtedly, such attitude is heavily influenced by the prediction capability of the prognostic method used.…”