Today in Sweden there are thousands of sensors used to check the state of train vehicles to detect faults. Almost all these sensors get separate measurements for every axle on a train so if an error is detected its location is defined by an axle number within a train. This axle number needs to be matched with a certain vehicle to be able to easily locate it and remove or at least check the vehicle. It is, therefore, necessary to be able to break down trains into vehicles from timestamps readings. This project explores the possibilities of using machine learning to classify the train vehicles based on timestamp readings made by RFID detector setups.Throughout the project, several algorithms were attempted with different structures and different ways of using the timestamp data. In the end, the MLP-neural network structure was most promising and a model that could predict 91% of the trains correctly was created. This model showed that machinelearning was a promising way to classify vehicles from axle timestamp readings. The model also worked for some of the faulting sensors. It worked since it did not require the entire RFID detector setup to be fully functional, which was an unexpected extra positive outcome of the project
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-198553 |
| Date | January 2022 |
| Creators | Hale, Oliver |
| Publisher | Umeå universitet, Institutionen för fysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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