Maintenance optimization in industry 4.0

被引:72
|
作者
Pinciroli, Luca [1 ]
Baraldi, Piero [1 ]
Zio, Enrico [1 ,2 ]
机构
[1] Politecn Milan, Energy Dept, Milan, Italy
[2] MINES Paris, PSL, Paris, France
基金
欧盟地平线“2020”;
关键词
Maintenance optimization; Industry; 4; 0; Knowledge information and data; Optimization approaches; Uncertain systems; ANALYTIC HIERARCHY PROCESS; OPPORTUNISTIC MAINTENANCE; MULTICOMPONENT SYSTEMS; DECISION-MAKING; MULTIOBJECTIVE OPTIMIZATION; GENETIC ALGORITHMS; WIND TURBINES; MODEL; RELIABILITY; STRATEGY;
D O I
10.1016/j.ress.2023.109204
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresh-olds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is iden-tified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the un-certainties affecting the behavior of the systems and the environment, and can jointly consider multiple opti-mization objectives, including the emerging ones related to sustainability and resilience.
引用
收藏
页数:19
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