This paper presents the implementation of a reference architecture for Cyber Physical Systems (CPS) to support Condition Based Maintenance (CBM) of industrial assets. The article focuses on describing how the MANTIS Reference Architecture is implemented to support predictive maintenance of clutch-brake assets fleet, and includes the data analysis techniques and algorithms implemented at platform level to facilitate predictive maintenance activities. These technologies are (1) Root Cause Analysis powered by Attribute Oriented Induction Clustering and (2) Remaining Useful Life powered by Time Series Forecasting. The work has been conducted in a real use case within the EU project MANTIS.
This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.
Cyber-Physical Systems constitute one of the core concepts in Industry 4.0 aiming at realizing production systems that combine the efforts of human workers, robots, and intelligent entities. This is particularly crucial in Human-Robot Collaboration manufacturing where a tight peer-to-peer interaction between humans and intelligent autonomous robots is necessary. The work proposes the integration of novel Artificial Intelligence technologies to enhance the flexibility and adaptability of collaborative robots. The integrated functionalities allow a collaborative robot to autonomously recognize the tasks a human worker performs, and accordingly adapt its behavior. The approach is deployed on a real HRC scenario showing the functioning of the developed cognitive capabilities and the increased flexibility of resulting collaborations.
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