Squats and corrugation detection of railway track based on time-frequency analysis by using bogie acceleration measurements

Wei, Xiukun; Yin, Xianxian; Hu, Yidong; He, Yong; Jia, Limin

doi:10.1080/00423114.2019.1610181

Cited by 26 publications

(10 citation statements)

References 20 publications

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“…Wei et al . [19,20] recognized rail corrugation by bogie and vehicle acceleration and verified it through field tests on Shanghai Metro Line 1. Sun and others [21] successfully identified the location, typical wavelength and severity of rail corrugations by measuring the in-vehicle noise on a high-speed railway.…”

Section: Introductionmentioning

confidence: 95%

Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective

Cai

Tang

Chang

et al. 2023

Phil. Trans. R. Soc. A.

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Rail corrugation is a common problem in metro lines, and its efficient recognition is always an issue worth studying. To recognize the wavelength and amplitude of rail corrugation, a particle probabilistic neural network (PPNN) algorithm is developed. The PPNN is incorporated with the particle swarm optimization algorithm and the probabilistic neural network. On the basis of the above, the in-vehicle noise characteristics measured in the field are used to recognize normal rail wavelengths of 30 and 50 mm. A stepwise moving window search algorithm suitable for selecting features with a fixed order was developed to select in-vehicle noise features. Sound pressure levels at 400, 500, 630 and 800 Hz of in-vehicle noise are fed into the PPNN, and the average accuracy can reach 96.43%. The bogie acceleration characteristics calculated by the multi-body dynamics simulation model are used to recognize normal rail amplitudes of 0.1 and 0.2 mm. The bogie acceleration is decomposed by the complete ensemble empirical mode decomposition with adaptive noise, and a reconstructional signal is obtained. The energy entropy of the reconstructional signal is fed into the PPNN, and the average accuracy can reach 95.40%. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

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Section: Introductionmentioning

confidence: 95%

Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective

Cai

Tang

Chang

et al. 2023

Phil. Trans. R. Soc. A.

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“…A substantial improvement of common crossing inspection system using machine learning prognostics and track-side monitoring is proposed in [31]. The study [32] describes a method for rail squats and corrugation detection using bogie acceleration measurements on in-service trains. The method uses an analysis of the feature frequencies from the continuous wavelet analysis and the feature modes from the empirical mode decomposition.…”

Section: Vol 15 Issue 2/2019 101-114mentioning

confidence: 99%

Multifractal Analysis of the Common Crossing Track-Side Measurements

Sysyn

Ižvolt

Nabochenko

et al. 2019

Civil and Environmental Engineering

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The deterioration common crossing elements increase the need for the maintenance of crossings and significantly reduce the reliability, availability and safety of railway traffic. The present paper introduces the results of experimental investigation of common crossing monitoring while its deterioration during the lifecycle of crossing. The conventional methods of common crossing condition estimation with time-based features like maximal accelerations, etc. are low appropriate as statistically significant condition indicators. The proposed multifractal analysis enables to extract the significant features from acceleration measurements. The extracted features are fused together with information about the train velocities and longitudinal position of wheels impact with the Lasso regularisation and multivariate linear regression. A ranking of feature importance is done and the redundant features are identified. The developed condition indicator has good correlation to the crossing lifetime and is simple for interpretation.

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“…These methodologies rely on advanced signal processing combined with machine learning techniques and are typically based on the extraction of proper features to distinguish undamaged and damaged situations. Previous studies demonstrate good results in railway defect detection using these approaches, namely, in the detection of train wheel damages, such as flats [ 26 , 27 , 28 , 29 , 30 , 31 ], out-of-roundness [ 32 ], and squats and corrugation [ 33 ]. Typically, these damage identification techniques require several operations including [ 34 , 35 ]: (i) data acquisition, (ii) feature extraction, (iii) feature normalization, (iv) feature fusion, and (v) feature classification.…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

Silva

Guedes

Ribeiro

et al. 2023

Sensors

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The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.

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Squats and corrugation detection of railway track based on time-frequency analysis by using bogie acceleration measurements

Cited by 26 publications

References 20 publications

Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective

Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective

Multifractal Analysis of the Common Crossing Track-Side Measurements

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

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