Autonomous maintenance of infrastructure, rolling stock and interfaces
Algorithms to detect and quantify defects of an S&C by means of on-site measurements data.
Goal
Develop algorithms to detect and quantify defects of an S&C by means of on-site measurements data.
Project Status and Results
The project builds on previous research results and data obtained from DigiSwitch, at Luleå University of Technology. The project aims to design algorithms, approaches, technologies and methodologies based on using feature extraction and artificial intelligence technologies to perform anomaly detection on Railway S&Cs. The methods and concepts on which the project will be based on are: machine learning, signal processing and vibration sensors.
Sponsor: Trafikverket
Researchers: Matti Rantatalo (PL), Jan Lundberg, Taoufik Najeh, Yang Zou (PhD candidate)
Duration: 2021-22
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