AI-driven Prognostics for Industrial Systems

About this programme

This course is designed for engineers, scientists, operators, and managers interested in utilizing AI-based methods for condition monitoring and prognostics in industrial systems and high-value assets. Participants will learn to identify common failure causes and predict Remaining Useful Life (RUL) using historical data, involving tasks such as data processing, feature selection, model development, and uncertainty quantification. Led by experienced professionals from industry and academia, the course covers the basics of prognostics and introduces various AI methods, including deep learning. It represents state-of-the-art AI-driven prognostic techniques, advanced signal processing, and feature engineering methods. Distance learning Mandatory sessions on campus: 0 Mandatory online sessions: 0 Optional sessions on campus: 0 Optional online sessions: 0 30 credits in engineering/technology and at least 2 years' experience in full-time employment in a relevant area within industry, or At least 3 years' experience in full-time employment in a relevant area within industry. 30 credits in engineering/technology and at least 2 years' experience in full-time employment in a relevant area within industry, or At least 3 years' experience in full-time employment in a relevant area within industry.