AI-driven Prognostics for Industrial Systems

Education facts

Code: ERA324

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.

Entry requirements

90 credits in engineering/technology, or 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. In addition, English 6 or English level 2 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

  Ortsoberoende

  Start date: 18 January 2027

  End date: 28 March 2027

  Pace of study: 25 %