With the rapid advancements in artificial intelligence, infrastructure and mechanical systems monitoring and maintenance can now leverage suitable AI algorithms to achieve both economic and labor efficiencies. As part of the larger Metint project, my PhD project aims to continuously monitor railway vehicles and infrastructure by applying the latest data science and machine learning techniques.
The focus of my work is to mine the data which is from the measuring system installed on a regularly operated passenger train and develop algorithms that provide information on the status of railway catenaries and train bogies. The measuring system is designed to work simultaneously with the train's daily run, avoiding interrupting train traffic.
Through my PhD work, I will contribute to the Metint project's goal of determining the trainset and railway infrastructure condition by analyzing data from onboard systems on Norwegian passenger trains. The outcome of my work will assist the railway industry in transitioning from traditional corrective maintenance to predictive maintenance, achieving greater efficiency and reducing costs.
Research areas and interests
Deep Learning, Machine Learning, Artificial Intelligence, Data Mining, Data Science