Selective maintenance model and its solution algorithm for multi-state series-parallel system under economic dependence

2016 ◽  
Author(s):  
Qingzheng Xu ◽  
Lemeng Guo ◽  
Na Wang
Keyword(s):  
Parallel System ◽  
Solution Algorithm ◽  
Economic Dependence ◽  
Selective Maintenance ◽  
Maintenance Model

scholarly journals Selective maintenance problem for series–parallel system under economic dependence

2016 ◽  
Vol 12 (5) ◽  
pp. 388-400 ◽  
Author(s):  
Qing-zheng Xu ◽  
Le-meng Guo ◽  
He-ping Shi ◽  
Na Wang
Keyword(s):  
Parallel System ◽  
Economic Dependence ◽  
Selective Maintenance ◽  
Maintenance Problem

scholarly journals A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms

2021 ◽  
Vol 16 (3) ◽  
pp. 372-384
Author(s):  
E.B. Xu ◽  
M.S. Yang ◽  
Y. Li ◽  
X.Q. Gao ◽  
Z.Y. Wang ◽  
...  
Keyword(s):  
Rest Period ◽  
Optimization Method ◽  
Parallel System ◽  
Maintenance Cost ◽  
Nsga Ii ◽  
Allocation Model ◽  
Multi Objective ◽  
Objective Model ◽  
Selective Maintenance ◽  
Personnel Allocation

Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

2021 ◽  
pp. 095440892199693
Author(s):  
Xinlong Li ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Hui Yu
Keyword(s):  
Decision Maker ◽  
Engineering Practice ◽  
Quality Uncertainty ◽  
Equipment Operation ◽  
System Maintenance ◽  
Selective Maintenance ◽  
Maintenance Decision ◽  
Series Systems ◽  
Maintenance Model

In order to improve the lifetime and reliability of equipment operation stage, it is necessary to carry out maintenance for components. The typical selective maintenance model does not consider the maintenance quality uncertainty and the failure effects, which may make the decision-maker overestimates the reliability of completing the next task after system maintenance, resulting in incorrect maintenance decision and prone to serious consequences of failure. In this paper, the potential discrepancy between the target maintenance quality and the actual maintenance quality of the decision maker is considered. At the same time, a multi-state FMECA is proposed to measure the failure effects under different states. Finally, the selective maintenance model of the multi-state series systems is established and solved by the genetic algorithm. The results show that considering the effect of component failure and the uncertainty of maintenance quality has an influence on maintenance decision. The maintenance decision made in this way is more consistent with the engineering practice.

Selective maintenance optimization for random phased-mission systems subject to random common cause failures

2018 ◽  
Vol 233 (3) ◽  
pp. 379-400 ◽  
Author(s):  
Xisheng Jia ◽  
Wenbin Cao ◽  
Qiwei Hu
Keyword(s):  
Single Phase ◽  
Failure Behavior ◽  
Maintenance Optimization ◽  
Simulation Approach ◽  
Common Cause ◽  
Proposed Model ◽  
Common Cause Failures ◽  
Selective Maintenance ◽  
Mission System ◽  
Maintenance Model

In both industrial and military fields, there is such a kind of complicated system termed as phased-mission system, which executes missions composed of several different phases in sequence. The structure, failure behavior, and working conditions of such a system may change from phase to phase. The duration of each phase of such a system involved is random and follows a probability distribution, and the system may suffer some events resulting in simultaneous failures of different elements with different probabilities. In order to guarantee such a system completes the phased-mission successfully, a selective maintenance model for random phased-mission systems subject to random common cause failures is proposed to optimally identify a subset of maintenance activities to be performed on some elements of the system. Thereinto, a novel analytic model is developed to estimate the probability of the maintained random phased-mission system successfully completing the phased-mission, and we compare it with a well-known Monte Carlo Simulation approach. Finally, the proposed selective maintenance model has been successfully applied to an artillery weapon system. Comparative analysis is carried out to compare the proposed model with the traditional ones, including selective maintenance models for deterministic phased-mission systems and deterministic single-phase mission systems. The results show that ignoring some mission properties (e.g. randomness and multiple phases) in selective maintenance optimization will lead to (1) incorrect system and mission modeling, (2) incorrect computation of the probability of the random phased-mission system successfully completing a mission, and/or (3) nonoptimal selective maintenance options.

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