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- W2914426267 abstract "Abstract The track condition can be negatively influenced by both weathering factors as well as traffic loads. As a result, this can lead to a reduction of track stiffness and a degradation of the track geometry. For an optimized maintenance strategy, the monitoring of the track through robust sensors mounted on in-service trains can be a purposeful opportunity. With the continuous measuring of axle-box accelerations, several short wavelength defects can be identified, for example, rail defects and local irregularities like mud spots. Therefore, algorithms that can detect and classify track failure based on acceleration signals must be developed. Local irregularities can lead to a degradation of the track geometry in a short period of time. As a result, high maintenance costs arise caused by train operation disruption and expensive reconstruction methods. Hence, local irregularities (mud spots) should be detected as early as possible. They are characterized by a weak formation below the ballast combined with pronounced and distinctive track irregularities. To simulate such a local irregularity and its typical characteristics, a track-vehicle scale model was built to generate acceleration data for different failure. Thereafter, the similarity between the model and the real system is investigated by dimensional analysis. Finally, five different methods are used to identify a mud spot from the measured acceleration. The advantages and disadvantages of the different methods in terms of failure detection are explained." @default.
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- W2914426267 date "2019-06-01" @default.
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- W2914426267 title "Track-vehicle scale model for evaluating local track defects detection methods" @default.
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- W2914426267 doi "https://doi.org/10.1016/j.trgeo.2019.01.001" @default.
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