22. Machine learning for railway vehicle and track monitoring

 

Benjamin Baasch

 The data science working group Asset Monitoring and Management at the Institute of Transportation Systems is doing research on new data-driven approaches for the condition monitoring of the traffic infrastructure to enable predictive maintenance. Machine learning is applied to derive condition information from embedded wayside and vehicle-mounted sensors. Our research is based on real world data examples gathered together with industry partners in relevant operational environment.

What is the data science project’s research question?   How can machine/deep learning methodologies be used to analyse sensor data for condition monitoring of railway vehicles and tracks?

What data will be worked on?   Multi-component acceleration data from in-service railway vehicles.

What tasks will this project involve? 

• Review of state-of-the-art machine learning models for time series analysis
• Signal processing
• Exploratory time series analysis
• Development of machine/deep learning pipelines in python
• Validation of results

What makes this project interesting to work on?  The project offers the chance to work on the emerging field of deep leaning for time series analysis. Data from real world scenarios will be used.

What is the expected outcome?   Contribution to research paper, Contribution to software development

What infrastructure, programs and tools will be used? Can they be used remotely? 

• Python
• Open source machine and deep learning frameworks
• High performance cluster
Infrastructure can be used remotely.

What skills are necessary for this project?   Data analytics / statistics, Scientific computation, Data mining / Machine learning, Deep learning

Is the data open source?  No

Interested candidates should be at Phd level. Benjamin Baasch is looking for 1 visiting scientist, working on the project together with the team.