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dc.contributor.advisorHalstensen, Maths
dc.contributor.authorJinia, Dilruba Tariq
dc.date.accessioned2022-08-15T16:41:20Z
dc.date.available2022-08-15T16:41:20Z
dc.date.issued2022
dc.identifierno.usn:wiseflow:6594776:50524736
dc.identifier.urihttps://hdl.handle.net/11250/3011948
dc.description.abstractWith rail-vehicle transport considered as greener and more sustainable means of travel in recent global times, there is a need for study and exploration of a real-time monitoring system for rail and its vehicle structures, that can facilitate identification of track defects and the vehicle’s structural monitoring. The method proposed for this is acousticchemometrics, is a data-driven approach, where acoustics, or vibrational data iscollected across three orthogonal axes and analyzed for structures or patterns that can suggest and/or indicate any faults or irregularities as can validate some of the hypotheses proposed by from Cemit. With an objective to understand and apply the mechanism of acoustic chemometrics for such purposes, pre-existing acoustic data from Cemit’s data collector has been used. The accelerometer data was converted from time-domain to frequency domain, and plotted across a spectrum to have an initial understanding of the dominant frequencies at play. Based on visual observations of the existing dominant frequencies, unusual or interesting spots along the original track of Brevikbanen, which was the track used for experiment, was suggested for deeper analysis. An initial chemometric analysis on averaged spectra was performed as an exploratory analysis, and modifications were suggested for more productive and specific chemometric analysis. The need for more experiments to produce reference data is also talked of, and a brief review of few studies that has worked with modelling and simulating track defects and rail-wheel interactions were also brought into light.
dc.languageeng
dc.publisherUniversity of South-Eastern Norway
dc.titleReal time monitoring of train wheels and track conditions based on acoustic measurements and multivariate data analysis
dc.typeMaster thesis


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