Asset criticality and risk prediction via machine learning in wind farms: problem-based educational activities in a smart industry operations course

Abstract

Smart industry and Industry 4.0 are terms which are often used interchangeably. They characterise industry that capitalises on optimising processes through the successful integration of advanced digitalisation and manufacturing technologies, while applying sound organisation and human factors management principles. Equipping the current and future generation professionals with the necessary skills and personal qualities needed for the transition to Industry 4.0, and its extension to Industry 5.0 has been targeted by academic and professional education. Lessons learned from existing studies point to problem-based learning as an effective mechanism for the internalisation of interdisciplinary concepts, methods, and technologies. This paper outlines the formulation and experience gained from educational activities within the context of a smart industry postgraduate MSc course. The aim was to bring together methods for process and data integration, technologies such as machine learning, and management aspects, targeting domains relevant to smart industry. An educational activity was designed relevant to risk prediction within the asset management of wind farms. With scenarios of diverse criticality assumptions, marking the importance of Industry 5.0, results obtained from the educational activity show that students excelling in individual dimensions of smart industry are valuable contributors in a team setting, but a sound holistic understanding and competences across all three pillars of smart industry are needed for best learning objectives.