Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high frequency acoustic emission signals

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@article{4bca73adff8340a395b2e8ba25f7fabb,
title = "Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high frequency acoustic emission signals",
abstract = "Typical railway wheelsets consist of the wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and online condition monitoring of rolling stock to reduce the probability of failure as much as possible. Current wayside systems such as hot axle box detectors and acoustic arrays can fail to detect defective bearings. This paper discusses the results of wayside high-frequency Acoustic Emission (AE) measurements carried on freight wagons with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis (TSK) is applied for the analysis of the AE data. From the results obtained it is evident that TSK is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel-rail interaction and changes in train speed.",
keywords = "Acoustic emission , wayside condition monitoring, axle bearing , time spectral kurtosis",
author = "Arash Amini and Mani Entezami and Zheng Huang and Hamed Rowshandel and Mayorkinos Papaelias",
year = "2016",
month = nov
day = "1",
doi = "10.1177/1687814016676000",
language = "English",
volume = "8",
journal = "Advances in Mechanical Engineering",
issn = "1687-8132",
publisher = "SAGE Publications",
number = "11",

}

RIS

TY - JOUR

T1 - Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high frequency acoustic emission signals

AU - Amini, Arash

AU - Entezami, Mani

AU - Huang, Zheng

AU - Rowshandel, Hamed

AU - Papaelias, Mayorkinos

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Typical railway wheelsets consist of the wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and online condition monitoring of rolling stock to reduce the probability of failure as much as possible. Current wayside systems such as hot axle box detectors and acoustic arrays can fail to detect defective bearings. This paper discusses the results of wayside high-frequency Acoustic Emission (AE) measurements carried on freight wagons with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis (TSK) is applied for the analysis of the AE data. From the results obtained it is evident that TSK is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel-rail interaction and changes in train speed.

AB - Typical railway wheelsets consist of the wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and online condition monitoring of rolling stock to reduce the probability of failure as much as possible. Current wayside systems such as hot axle box detectors and acoustic arrays can fail to detect defective bearings. This paper discusses the results of wayside high-frequency Acoustic Emission (AE) measurements carried on freight wagons with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis (TSK) is applied for the analysis of the AE data. From the results obtained it is evident that TSK is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel-rail interaction and changes in train speed.

KW - Acoustic emission

KW - wayside condition monitoring

KW - axle bearing

KW - time spectral kurtosis

U2 - 10.1177/1687814016676000

DO - 10.1177/1687814016676000

M3 - Article

VL - 8

JO - Advances in Mechanical Engineering

JF - Advances in Mechanical Engineering

SN - 1687-8132

IS - 11

ER -