The main goal of the eMaintenance Research Program is to enable Operational Excellence enhanced through establishment of effective and efficient operation and maintenance processes. The program enables Augmented Decision-Making empowered by Advanced Maintenance Analytics. Frameworks, approaches, methodologies, technologies, and tools such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), eXplainable AI (XAI), service-oriented and event-oriented approaches, digitalisation, IoT and IIoT, and information logistics are getting orchestrated to develop solutions which can be utilised during a system’s whole lifecycle (i.e. conceptualisation, design, development, production, utilisation, and retirement). This can be applied to various process phases (i.e. maintenance management, maintenance support planning, maintenance planning, maintenance execution, maintenance assessment, and maintenance improvement) on-line and in real-time.
The overarching objective is to:
- conduct a multi-disciplinary applied research in maintenance analytics;
- develop and provide an appropriate education platform in eMaintenance;
- establish an innovation process which supports implementation of research outcomes to real-world solutions.
The program focuses on topics which reflect issues and challenges within industry and academia. Some of these topics are: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), eXplainable AI (XAI), service-oriented and event-oriented approaches, digitalisation, IoT and IIoT, Big Data Analytics, cloud-computing, distributed computing, crowd-computing, information logistics, data integration, data fusion, data processing, data visualisation, and context adaptation.
The programme also aims to design, develop, and provide artefacts based on edge technology to demonstrate proof-of-concept within the aforementioned topics. The main objective of these demonstrators are to validate academic outcome in industrial contexts. This is achieved in collaboration with with eMaintenance LAB.