Condition monitoring of railway track from car-body vibration using time–frequency analysis

Tsunashima, Hitoshi; Hirose, Ryota

doi:10.1080/00423114.2020.1850808

Cited by 37 publications

(21 citation statements)

References 21 publications

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“…Tsunashima et al proposed techniques of condition monitoring of railway tracks based on time-frequency analysis [10]. They compared the performance of Hilbert-Huang transforms (HHT) and CWT for identifying track faults from car body vibration.…”

Section: Literature Review Of Track Condition Monitoring Using Machin...mentioning

confidence: 99%

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Monitoring the Condition of Railway Tracks Using a Convolutional Neural Network

Tsunashima¹,

Takikawa²

2022

Recent Advances in Wavelet Transforms and Their Applications

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Condition monitoring of railway tracks is effective for the sake of an increase in the safety of regional railways. This study proposes a new method for automatically classifying the type and degradation level of track fault using a convolutional neural network (CNN), which is a machine learning method, by imaging car body acceleration on a time-frequency plane by continuous wavelet transform. As a result of applying this method to the data measured in regional railways, it was possible to classify and extract the sections that need repair according to the degree of deterioration of the tracks, and to identify the track fault in those sections.

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Section: Literature Review Of Track Condition Monitoring Using Machin...mentioning

confidence: 99%

“…The vehicle model used in the simulation is shown in Figure 2 [10]. The vehicle model consists of a total of seven rigid bodies: one car body, two bogies, and four wheelsets.…”

Section: Vehicle Model Used In the Simulationmentioning

confidence: 99%

Monitoring the Condition of Railway Tracks Using a Convolutional Neural Network

Tsunashima¹,

Takikawa²

2022

Recent Advances in Wavelet Transforms and Their Applications

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“…3 First, car-body vibration includes both vibrations caused by track irregularities and those caused by the vehicle condition. Although track irregularities are closely correlated with car-body vibrations, 13 there are many nonlinear relationships and influencing factors between track irregularities and car-body vibration 9 which exhibit uncertainty and heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Development of metro track geometry fault diagnosis convolutional neural network model based on car-body vibration data

Wang

Liu

Wang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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Traditional track geometry fault diagnosis methods, such as track geometry car, track detection trolley, and manual measurement, suffer from low detection frequency. Therefore, using vibration data from in-service metro vehicles to diagnose metro track geometry faults can improve the efficiency of metro track condition detection. Hence, a metro track geometry fault diagnosis model using car-body vibration was established in this study based on a convolutional neural network model, and the effects of the structural hyperparameters on the model performance were analysed. Firstly, the problem of car-body vibration data collection was addressed, and a mileage-locating model without the global navigation satellite system was developed. Then, the metro track geometry fault diagnosis model was derived using a convolutional neural network whose architecture is determined by four hyperparameters. Finally, a case study was performed using the Beijing Subway Line 1 track geometry car detection data and car-body vibration data. The results demonstrated the effectiveness of the proposed model.

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“…In a more recent work, Tsumashima [12] has develop a method to detect and isolate track faults that are later classified using machine learning techniques. Tsumashima and Hirose [13] have applied the time-frequency Hilbert-Huang Transform method to identify track faults using the measured car-body acceleration as the input.…”

Section: Introductionmentioning

confidence: 99%

A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Escalona

Urda

Muñoz

2021

Sensors

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This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

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Condition monitoring of railway track from car-body vibration using time–frequency analysis

Cited by 37 publications

References 21 publications

Monitoring the Condition of Railway Tracks Using a Convolutional Neural Network

Monitoring the Condition of Railway Tracks Using a Convolutional Neural Network

Development of metro track geometry fault diagnosis convolutional neural network model based on car-body vibration data

A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

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