Uncertainty Detection in Elevators or Trains

Developing methods based on AI techniques to discover unforeseen situations in Elevators or Train.
Master

This topic focuses on developing methods to discover unforeseen situations in systems during their design and operation. The topic involves applying AI techniques on historical data (time series-based) and live streaming data to discover unpredictable situations. There are real case studies available: 1) Elevators provided by Orona in Spain; 2) Train Control and Management System provided by Bombardier Transportation.

Goal

To discover unforeseen situations in real systems, to prevent them from failures.

Learning outcome

You will gain knowledge about applied artificial intelligence techniques and working with real systems.

Qualifications

Some programming and modeling skills. Knowledge of machine learning frameworks such as PyTorch and Keras is an added advantage.

Supervisors

  • Shaukat Ali
  • Tao Yue

Collaboration partners

  • Orona and Mondragon University, Spain
  • Mälardalens högskola and Bombardier Transportation, Sweden

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