Maintenance 4.0 World of Integrated Information [chapter]

Erkki Jantunen, Giovanni Di Orio, Csaba Hegedűs, Pal Varga, István Moldován, Felix Larrinaga, Martin Becker, Michele Albano, Pedro Maló
2019 Proceedings of the I-ESA Conferences  
The Condition-Based Maintenance (CBM) strategy has got new, powerful toolset recently: the concepts of Internet of Things (IoT) and Cyber-Physical Systems (CPS). These can provide flexible but powerful data collection and analysis methods for Proactive and Predictive Maintenance. In the landscape of new digitalization and interconnection of products, services, processes, enterprises and people, IoT/CPS-based platforms are increasing in their size and target applications in a steady manner.
more » ... e the fundamental research challenges regarding the reference architecture, interoperability, performance, quality and deployment issues, the challenges regarding system maintenance are also burning. There are various issues that are specific to the maintenance domain: interoperability and data flow management, data representation models, and data processing models and tools. The paper describes a maintenance reference architecture and platform, which aims to tackle all these challenges. The architecture suggested by the MANTIS project covers edge and cloud level interoperability, data flow management, and data processing issues. Furthermore, it provides domain-specific methods for root cause analysis, failure prediction, and models for predicting remaining useful life. The architecture is strengthened by the concept of MIMOSA, a data model definition that allows data representation models that are easy to fit into relational object and information management models required by CBM. The MANTIS platform utilizes the Arrowhead framework for tackling interoperability and integrability issues. Abstract. The Condition-Based Maintenance (CBM) strategy has got new, powerful toolset recently: the concepts of Internet of Things (IoT) and Cyber-Physical Systems (CPS). These can provide flexible but powerful data collection and analysis methods for Proactive and Predictive Maintenance. In the landscape of new digitalization and interconnection of products, services, processes, enterprises and people, IoT/CPS-based platforms are increasing in their size and target applications in a steady manner. Beside the fundamental research challenges regarding the reference architecture, interoperability, performance, quality and deployment issues, the challenges regarding system maintenance are also burning. There are various issues that are specific to the maintenance domain: interoperability and data flow management, data representation models, and data processing models and tools. The paper describes a maintenance reference architecture and platform, which aims to tackle all these challenges. The architecture suggested by the MANTIS project covers edge and cloud level interoperability, data flow management, and data processing issues. Furthermore, it provides domain-specific methods for root cause analysis, failure prediction, and models for predicting remaining useful life. The architecture is strengthened by the concept of MIMOSA, a data model definition that allows data representation models that are easy to fit into relational object and information management models required by CBM. The MANTIS platform utilizes the Arrowhead framework for tackling interoperability and integrability issues.
doi:10.1007/978-3-030-13693-2_6 fatcat:ilmmwkuwlje3fmfwa7n3rgzb5m