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  • 1.
    Jonsson, Markus
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics.
    Heberlein, Mattis
    Luleå University of Technology, Department of Engineering Sciences and Mathematics.
    Spårbunden Tillståndsövervakning av tåg med IoT lösning: För snabbare utveckling av detektor-prototyper inom IoT2020Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The railway system in Sweden is extensive and needs hundreds of different detectors to measure the state-based maintenance. The maintenance must be made more efficient and the railway system needs to expand if the demands on railway-going transport shall increase. But today’s detectors are expensive and, in some cases, out of age. At the same time as today’s electrical components are small and powerful. Here could the hardware be built together with the programmed software and then be put out in the field.

    This examining work was tasked with examining if it was possible to use an IoT solution to replace the current monitoring system for impact noise and heat from the trains wheels and wheel bearings on Swedish trains. It was also examined if autocorrelation could be useful for detecting these impact noises from the train wheels. Different IoT platforms, based on Arduino and/or Raspberry Pi, was examined and how it communicated with the IoT-cloud, as well as what sensor are to be used for collecting the necessary data. It was determined that a platform based on the Arduino WiFi rev 2 coupled with an electret microphone and an infrared heat sensor was the most viable solution. Using autocorrelation to determine impact noise was also theoretically proven to be a viable method, but it could not be proven if it was applicable in real life since no field tests of the prototype was performed.

    The prototype did ultimately cost 660 SEK and was able to store information locally on a microSD card, communicate with the internet with the help of IBM-Watson and Node-RED and manage the Nordic climate.

    In addition, thanks to this work, a detector was placed out in the field to collect audio information from passing trains, which could be analysed using MATLAB. These field-tests showed that digital filtering of sound data can determine the number of wagons and frequency domains.

    Continued work after this project may include tests with faster sensors, a cloud-based data management and further investigations for whether the autocorrelation can function as an algorithm for recognizing impact sound. Various variants of filtering for clearer signal will also be interesting for further work.

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