Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements

Although track maintenance is important, many operators of regional railway with limited financial resources are unable to conduct sufficient track inspections. In response to this problem, a track condition diagnosis system using car-body vibration sensors has been developed. In this study, a track condition monitoring system using a smartphone for general use has been developed. A technique for identifying train location using global navigation satellite system (GNSS) speed is proposed. The results of field testing shows that track condition diagnosis is possible using a smartphone-based monitoring system.

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“…Bai et al used low-cost accelerometers that were placed on or attached to the floors of operating trains for analyzing track quality [16].…”

Section: Car-body-mounted Sensorsmentioning

confidence: 99%

Track Condition Monitoring Based on In-Service Train Vibration Data Using Smartphones

Tsunashima¹,

Honda²,

Matsumoto³

2024

Civil Engineering

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“…The ways for further improvement of prognosis have been analyzed. A model for data-driven bias correction and defect diagnosis for in-service vehicle acceleration measurements is presented in the research [ 92 ]. The model consists of two parts: a bias correction sub-model, and a defect diagnosis sub-model for in-service vehicle acceleration measurements.…”

Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

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The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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“…Schwanen et al proposed a method to detect squats by listening to rolling noise using microphones [14]. Bai et al used low-cost accelerometers that were placed on or attached to the floors of operating trains for analysing track quality [15]. A Kalman filter-based method to estimate the track geometry of Shinkansen tracks from car-body motions was proposed [16,17].…”

Section: Trends Of Track Condition Monitoringmentioning

confidence: 99%

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

Tsunashima

Hirose

2020

Vehicle System Dynamics

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Proper maintenance of railway tracks is essential to ensure railway safety. A track condition monitoring system was developed for the preventive maintenance of the track by installing the on-board sensing device in the in-service vehicles and monitoring the track condition by measuring the car-body acceleration. This paper describes the application of time-frequency analysis for the condition monitoring of railway tracks. Car-body acceleration simulated by a 10-DOF vehicle model, with a faulty track, was used to identify the track faults using Hilbert-Huang transform (HHT). Field tests were carried out using the developed track condition monitoring system to show the effectiveness of the time-frequency analysis using HHT. Simulation studies and field test results showed that HHT can yield a good time-frequency resolution and that intrinsic mode functions (IMFs) can provide the detailed information of track faults.

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Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements

Cited by 10 publications

References 26 publications

Track Condition Monitoring Based on In-Service Train Vibration Data Using Smartphones

Track Condition Monitoring Based on In-Service Train Vibration Data Using Smartphones

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

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

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