Optimal Selective Maintenance Strategy for Multi-State Systems Under Imperfect Maintenance

Abstract

Abstract-Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. In such a case, one of the most widely used maintenance policies is a selective maintenance in which a subset of feasible maintenance actions is chosen to be performed with the aim at achieving the subsequent mission success under limited maintenance resources. Traditional selective maintenance optimization reported in the literature only focuses on binary state systems. Most systems in industrial applications, however, have more than two states in the deterioration process. In this work, a selective maintenance policy for multi-state systems (MSS) consisting of binary state elements is investigated. Taking the imperfect maintenance quality into consideration, the Kijima model is reviewed, and a cost-maintenance quality relationship which considers the age reduction factor as a function in terms of maintenance cost is established. Moreover, with the assistance of the universal generating function (UGF) method, the probability of the repaired MSS successfully completing the subsequent mission is formulated. In place of enumerative methods, a genetic algorithm (GA) is employed to solve the complicated optimization problem where both multi-state systems, and imperfect maintenance models are taken into account. The effectiveness of the proposed method is demonstrated via a case study of a power station coal transportation system. Finally, a comparative analysis between the strategies with and without considering imperfect maintenance is conducted, and it is concluded that incorporating imperfect maintenance quality into selective maintenance achieves better outcomes.

A combined method for reliability analysis of multi-state system of minor-repairable components

“…According to the maintenance strategy structure considered in [43], the size of the system-level strategy space will be also able to reach more than 100 million which is too large to be processed by the general enumeration method, where the system contains only 6 subsystems and 21 components with not more than 6 states. This "combinatorial explosion" situation can be also encountered in [41,11,38] etc.…”

Selective Maintenance Optimization for a Multi-State System Considering Human Reliability

“…, M denote the maintenance worker who performs all the maintenance tasks. Then, according to equations (8) to (12), we can calculate the human error probability P m of maintenance worker m by:…”

“…According to equations (1) to (3) discussed in Section 2, the UGF of the overall system can be expressed by: The state distribution of components after the next mission is given by the universal generating function (UGF) (see [12,14,31]). The UGF is defined by Liu et al [12] as a polynomial function to represent the probability mass function of a discrete random variable. For component i, the performance rate distribution at time t can be represented as:…”

Progressive system safety and reliability analysis: A sustainable game theory approach

“…Also, similar to many application domains, researchers have been attempting to integrate game theory into SSRA by highlighting that the game theory model has considerable advantages over conventional system safety and reliability techniques, including but not limited to those of. [24][25][26][27][28][29][30][31][32][33][34][35][36][37] Three examples include the dependent failures at the early life of a system, analyzed by cooperative game theory. 38 Gao et al 18 have assessed the impact of decision-making errors on strategies of software detection.…”