Reliability-based selective maintenance for redundant systems with dependent performance characteristics of components

The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-Copulabased reliability evaluation method is proposed to estimate the reliability of system components with multiple PCs.Specifically,the marginal degradation reliability of each PC is built by using the Wiener stochastic process based on the PC’s degradation mechanism.The joint degradation reliability of the component with multiple PCs is established by connecting the marginal reliability of PCs using D-vine.In addition,two RSM decision models are developed to ensure the system accomplishes the next mission.The genetic algorithm(GA)is used to solve the constraint optimization problem of the models.A numerical example illustrates the application of the proposed RSM method.

Selective maintenance problem for series–parallel system under economic dependence

In view of the high complexity of the objective world, an economic dependence between subsystems(paired and unpaired) is proposed, and then the maintenance cost and time under different economic dependences are formulated in a simple and consistent manner. Selective maintenance problem under economic dependence(EDSMP) is presented based on a series–parallel system in this paper. A case study shows that the system reliability is promoted to a certain extent, which can validate the validity of the EDSMP model. The influence of the ratio of set-up cost on system performance is mainly discussed under different economic dependences. Several existing improvements of classical exhaust algorithm are further modified to solve a large sized EDSMP rapidly. Experimental results illustrate that these improvements can reduce CPU time significantly.Furthermore the contribution of each improvement is defined here, and then their contributions are compared thoroughly.

Recent Advances in Selective Maintenance from 1998 to 2014

The selective maintenance is a new branch and significant breakthrough of reliability and maintenance theory.In the original selective maintenance problem,a subset of maintenance activities is performed on selected components during the finite break so that the system is able to maximize the next mission reliability.It is a fast growing research field over the past ten years,in which a variety of mathematical models and solution methodology have been proposed for dealing with more complex and significant problems.An overview of some recent advances in selective maintenance modeling and treatment methods is provided in this paper.A number of challenges that can be undertaken to help move the field forward are also discussed according to the current state of the selective maintenance approach.

A Multi-Level Selective Maintenance Strategy Combined to Data Mining Approach for Multi-Component System Subject to Propagated Failures

In several industrial fields like air transport,energy industry and military domain,maintenance actions are carried out during downtimes in order to maintain the reliability and availability of production system.In such a circumstance,selective maintenance strategy is considered the reliable solution for selecting the faulty components to achieve the next mission without stopping.In this paper,a novel multi-level decision making approach based on data mining techniques is investigated to determine an optimal selective maintenance scheduling.At the first-level,the age acceleration factor and its impact on the component nominal age are used to establish the local failures.This first decision making employed K-means clustering algorithm that exploited the historical maintenance actions.Based on the first-level intervention plan,the remaining-levels identify the stochastic dependence among components by relying upon Apriori association rules algorithm,which allows to discover of the failure occurrence order.In addition,at each decision making level,an optimization model combined to a set of exclusion rules are called to supply the optimal selective maintenance plan within a reasonable time,minimizing the total maintenance cost under a required reliability threshold.To illustrate the robustness of the proposed strategy,numerical examples and a FMS real study case have been solved.

Research on Selection of Equipment Maintenance Modes

The development of equipment maintenance management is introduced, and equipment maintenance concept is defined. Equipment maintenance modes are classified, analyzed and compared, which merits and demerits are pointed out. At last, a decision-making frame to select equipment maintenance modes is advanced, and steps to select and implement equipment maintenance are given.

Research on Network Maintenance Strategy Selection Based on Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution Algorithm

Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process(AHP) and technique for order preference by similarity to ideal solution(TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.