Examensarbete: Locomotive wheel and brake block wear modelling using telemetry data
Green Cargo ABSammanfattning
This degree project focuses on modelling the wear of locomotive wheels and brake blocks using telemetry data, crucial for maintaining safety in freight locomotives. The project involves analyzing maintenance records and telemetry data to enhance predictive maintenance strategies, ultimately improving component utilization and reducing waste. The work can be conducted in Swedish or English and is based in Stockholm, with a duration of 10 weeks for a Bachelor’s degree or 20 weeks for a Master’s.Det här erbjuder vi
Opportunity to work on a practical project with real-world applications.Gain experience in predictive maintenance strategies in the railway sector.
Ansök senast: Öppet tillsvidare
Publicerad: 2026-10-22
Beskrivning
Locomotive wheel and brake block wear modelling using telemetry data
Background
For thread-braked freight locomotives, carbon strip wear and brake block wear are major maintenance concern. The condition of these components is critical for safe rail transport which in turn sets strict limits on the distance travelled between inspection. Hence, insight into the main driver affecting their lifespan would provide railway operators with valuable information to support condition-based or predictive maintenance. This would enable longer distance between inspection, better utilization of the components and less material waste. Some of Green Cargos more modern electric locomotives have on-board systems that collect the energy consumption and regeneration, current, voltage, speed, location, main pipe pressure and the brake cylinder pressure of the locomotive.
Problem description, tasks and goals
Use maintenance records to identify re-blocking and wheel turning. Combine telemetry data and maintenance data to model wear on wheels and blocks. Perform real-world measurements of block wear.
Category to which this degree project corresponds the best:
Vehicles
Type of degree project (can be both)☒ Master (20 weeks)
☒ Bachelor/Högskoleingenjör (10 weeks)
Language for the thesis☒ Swedish and/or ☒English
Is Swedish a language requirement?☒ No, but Swedish is a requirement for future employment.
Possibility to work from our office
☒ Yes No
Address: Hälsingegatan 49, 113 31 Stockholm
Contact person
Mattias Wang
Role: Vehicle/Maintenance Engineer
Please contact me if you have any questions: mattias.wang@greencargo.com
Background
For thread-braked freight locomotives, carbon strip wear and brake block wear are major maintenance concern. The condition of these components is critical for safe rail transport which in turn sets strict limits on the distance travelled between inspection. Hence, insight into the main driver affecting their lifespan would provide railway operators with valuable information to support condition-based or predictive maintenance. This would enable longer distance between inspection, better utilization of the components and less material waste. Some of Green Cargos more modern electric locomotives have on-board systems that collect the energy consumption and regeneration, current, voltage, speed, location, main pipe pressure and the brake cylinder pressure of the locomotive.
Problem description, tasks and goals
Use maintenance records to identify re-blocking and wheel turning. Combine telemetry data and maintenance data to model wear on wheels and blocks. Perform real-world measurements of block wear.
Category to which this degree project corresponds the best:
Vehicles
Type of degree project (can be both)☒ Master (20 weeks)
☒ Bachelor/Högskoleingenjör (10 weeks)
Language for the thesis☒ Swedish and/or ☒English
Is Swedish a language requirement?☒ No, but Swedish is a requirement for future employment.
Possibility to work from our office
☒ Yes No
Address: Hälsingegatan 49, 113 31 Stockholm
Contact person
Mattias Wang
Role: Vehicle/Maintenance Engineer
Please contact me if you have any questions: mattias.wang@greencargo.com
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Examensarbete: Locomotive wheel and brake block wear modelling using telemetry data
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