Abstract
In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS.
Highlights
For some systems that require the continuous execution of multiple missions, all the maintenance activities need to be performed during maintenance breaks
The statefor of after maintenance using a dynamic discrete distribution and a model proposed to calculate the human error probability (HEP). for selective maintenance model for an considering human reliability was investigated the first time in this paper
We formulated the relationships between the HEP and the state of method to determine the distribution of component states after maintenance was proposed
Summary
For some systems that require the continuous execution of multiple missions, all the maintenance activities need to be performed during maintenance breaks. For the selective maintenance problem, an optimal plan can save maintenance time and costs during the maintenance breaks and maximize the reliability of an MSS to perform the mission. We will study the selective maintenance problem for a multi-state series-parallel system considering human reliability. In order to estimate the different level of workers in the maintenance of multi-state components, we use performance influencing factors (PIFs) to calculate the human error probability (HEP). The MSS in this paper consists of multi-state components that are all repairable; All the maintenance activities are performed by one maintenance worker, and there is no maintenance activity during a mission; and.
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