AI-driven Prognostics for Industrial Systems

Education facts

Code: ERA324

<p>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. </p>

Entry requirements

75 credits in energy engineering, mechanical engineering, production technology, product and process development, computer technology and/or computer science or equivalent or 40 credits in technology and at least 2 years of full-time professional experience from a relevant area within industry. In addition, English A/English 6 is required.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• AI-driven Prognostics for Industrial Systems

  Mälardalen University

  Start date: 19 January 2026

  End date: 29 March 2026

  Pace of study: 25 %