Artificial Intelligence based Asset Management

被引：0

作者：

Mattioli, Juliette ^{[1
]}

Perico, Paolo ^{[2
]}

Robic, Pierre-Olivier ^{[3
]}

机构：

[1] Thales, La Defense, France

[2] McLaren Automot Ltd, Woking, Surrey, England

[3] Thales Global Serv, Velizy Villacoublay, France

来源：

2020 IEEE 15TH INTERNATIONAL CONFERENCE OF SYSTEM OF SYSTEMS ENGINEERING (SOSE 2020) | 2020年

关键词：

Asset Management; Maintenance; Supply Chain Management; Artificial Intelligence; Data driven AI; Machine Learning; Symbolic AI; Knowledge based AI; Multi-Criteria; Decision Making; Planning; Ontology; USEFUL LIFE ESTIMATION; MAINTENANCE; MODEL;

D O I：

10.1109/sose50414.2020.9130505

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In a System Engineering perspective, asset management (AM) is related to a subset of techniques focusing on the in-service phase, aligned with product life-cycle management discipline. Today, within AM solution market, the integration of Artificial Intelligence (AI) technics above traditional entreprise solution is a key trend. This paper is focusing on how symbolic AI and data driven AI could improve some issues of the AM life cycle, in particular in asset acquisition, performance analysis and forecasting, asset monitoring, predictive and prescriptive maintenance, supply chain optimisation including spare parts management...

引用

页码：151 / 156

页数：6

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