计算机科学
软件部署
物联网
工业4.0
相关性(法律)
风险分析(工程)
数据科学
过程管理
工程类
计算机安全
软件工程
数据挖掘
法学
医学
政治学
作者
Michele Compare,Piero Baraldi,Enrico Zio
标识
DOI:10.1109/jiot.2019.2957029
摘要
The Industry 4.0 paradigm is boosting the relevance of predictive maintenance (PdM) for manufacturing and production industries. PdM strongly relies on Internet of Things (IoT), which digitalizes the physical actions allowing human-to-human, human-to-machine, and machine-to-machine connections for intelligent perception. Several issues still need to be addressed for reaching the maturity stage for the widespread application of PdM. To do this, IoT needs to be empowered with data science capabilities, to reach the ultimate objective of digitalization, which is supporting decision making to optimally act on the physical systems. In this article, we present a comprehensive outlook of the current PdM issues, with the final aim of providing a deeper understanding of the limitations and strengths, challenges and opportunities of this dynamic maintenance paradigm. This is done through extensive research and analysis of the scientific and technical literature. On this basis, this article outlines some main research issues to be addressed for the successful development and deployment of IoT-enabled PdM in industry.
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