International Congress and Workshop on Industrial AI and eMaintenance 2023

This proceedings brings together the papers presented at the International Congress and Workshop on Industrial AI and eMaintenance 2023 (IAI2023). The conference integrates the themes and topics of three conferences: Industrial AI & eMaintenance, Condition Monitoring and Diagnostic Engineering Management (COMADEM) and, Advances in Reliability, Maintainability and Supportability (ARMS) on a single platform. This proceedings serves both academy and industry in providing an excellent platform for collaboration by providing a forum for exchange of ideas and networking.

The 21st century has seen remarkable progress in Artificial Intelligence, with application to a variety of fields (computer vision, automatic translation, sentiment analysis in social networks, robotics, etc.) The IAI2023 focuses on Industrial Artificial Intelligence, or IAI. The emergence of industrial AI applications holds tremendous promises in terms of achieving excellence and cost-effectiveness in the operation and maintenance of industrial assets. Opportunities in Industrial AI exist in many industries such as aerospace, railways, mining, construction, process industry, etc. Its development is powered by several trends: the Internet of Things (IoT); the increasing convergence between OT (operational technologies) and IT (information technologies); last but not least, the unabated fast-paced developments of advanced analytics. However, numerous technical and organizational challenges to the widespread development of industrial AI still exist.

The IAI2023 conference and its proceedings foster fruitful discussions between AI creators and industrial practitioners.

Covers industries such as aerospace, railways, mining, construction, and the process industry Encourages discussions between AI creators and industrial practitioners Demonstrates the opportunities and challenges of industrial AI and eMaintenence

Auteur

Uday Kumar, PhD, is the Chair Professor of Operation and Maintenance Engineering, and Director of Luleå Railway Research Center. He has more than 30 years of experience in consulting and finding solutions to industrial problems related to maintenance of engineering assets. He has a wide range of interests in equipment maintenance, reliability and maintainability analysis, product support, life cycle costing, risk analysis, system analysis, eMaintenance, and asset management. He has been involved in many EU framework projects, published more than 300 papers in International Journals and Conference Proceedings and co-authored four books on maintenance engineering and management. He is an elected member of the Swedish Royal Academy of Engineering Sciences.

Prof. Ramin Karim is a Professor of Operation and Maintenance Engineering at Luleå University and Director of the Centre of Intelligent Asset Management. He has 15 years of experience in IT industry after obtaining hisdegree in computer science. He is responsible for the eMaintenance LAB, an applied laboratory for research and innovation in Industrial AI and eMaintenance. His research interest includes data analytics and predictive technology closely connected to operation and maintenance of industrial assets.

Prof. Karim has initiated and developed the concept and platform called 'AI Factory', which is a universal concept for cross-industry data and model sharing based on digital and AI technologies. He is the founder of a spin-off company from LTU, which provides advanced analytics based on cloud/edge-technologies and artificial intelligence. Professor Karim has demonstrated how excellence in research can lead to sustainable development in society and benefits for business, through his innovative thinking and commitment to his research in digitalization, Industrial AI and eMaintenance. He has received recognition for his research results, and has been an in-demand lecturer and advisor in various contexts. His research results have been recognized two years in a row on IVA's 100 list in 2020 and 2021.

Contenu
Use cases of Generative AI in Asset Management of Railways.- A neuroergonomics mirror-based platform to enhance cognitive impairments in fighter pilots.- Risk-based safety improvements in railway asset management.- Performance of Reinforcement Learning in Molecular Dynamics Simulations: A Case Study of Hydrocarbon Dynamics.- Causal Effects of Railway Track Maintenance An Experimental Case Study of Tamping.- Self-Driving Cars in the Arctic Environment.- Towards a Railway Infrastructure Digital Twin Framework for African Railway Lifecycle Management. <p