Acoustic monitoring of rail-head roughness for targeted grinding and noise modelling

Jones, R.R.K.; Packham, A.J.

doi:10.1049/ic:20060045

Cited by 1 publication

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“…The railway noise can also be applied in the detection of rail-head roughness and rail structural health. Jones and Packham [26] studied the distribution characteristics between the rolling noise and the train speed by using a microphone mounted near a smooth wheel, where the noise ranges at different speeds can support the identification of rail-head roughness. Janeliukstis and Kaewunruen [27] found that the acoustic sources of railway noise can be separated from each other, and adopted a decision tree classification algorithm to monitor the structural health of railway pre-stressed concrete sleepers.…”

Section: Related Workmentioning

confidence: 99%

An Acoustic-Based Recognition Algorithm for the Unreleased Braking of Railway Wagons in Marshalling Yards

Zhang

Liang

2020

IEEE Access

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The abnormal braking of wagons is a challenging safety problem for operators at railway marshalling yard. This paper develops an acoustic-based technology to detect the unreleased braking of wagons during the uncoupling operation. Experiments have been conducted to collect the acoustic waves of wagons abnormal braking, as well as the background sounds like train whistling and wheel vibration. Before data collection, a wayside recording system and an experimental train composed of 5 different wagons have been prepared in the marshalling yard. The recognition algorithm consists of the fast Fourier transform (FFT), feature extraction, template matching and support vector machine (SVM) classification. Based on the sample data of different acoustic waves, the FFT is firstly performed to obtain the frequency spectrum from original time-domain signals. Then the major spectrum features of different sounds are carefully extracted for SVM training through a newly-devised algorithm, where the features include the spectrum center, spectrum flux, energy peak and corresponding frequency. During the SVM training, classifiers are designed under oneagainst-one strategy to guarantee the recognition accuracy. Given a test data, at most 3 SVM classifiers will be activated according to the decision matrix of template matching. Meanwhile, rules have been made to regulate the classification result considering different activation cases. Finally, a case study of all 12 sound categories has been performed to illustrate the application of proposed algorithm. Results show that the acoustic-based recognition algorithm is indeed reliable to identify wagons unreleased braking, with the global warning accuracy over 98%.

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Section: Related Workmentioning

confidence: 99%