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

作　　者：Mohamed Ali Kammoun Zied Hajej Nidhal Rezg 

机构地区：[1]Universite de Lorraine,LGIPM,F-57000 Metz,France [2]Ecole poly technique du Groupe Honoris United Universities-Tunisia

出　　处：《Journal of Systems Science and Systems Engineering》2022年第3期313-337,共25页系统科学与系统工程学报（英文版）

摘　　要：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.

关 键 词：Selective maintenance stochastic dependence age acceleration factor data mining flexible manufacturing system 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]