Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique

Hao, Xiaoli; Yang, Jian; Yang, Fei; Sun, Xianfu; Hou, Ya-Li; Wang, Jian

doi:10.1080/00423114.2022.2037669

Cited by 16 publications

(3 citation statements)

References 23 publications

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“…Wavelengths shorter than 5 m, however, could not be captured due to the filtering effect of the suspensions and were removed with a high pass filter. Hao et al [39] also made use of deep learning techniques for assessment of the longitudinal level of high-speed railway lines in China. The authors proposed a NN model combining an Attention Mechanism (AM), a Convolution Neural Network (CNN) and a Gated Recurrent Unit (GRU), which was trained using measured data from a specialized inspection vehicle.…”

Section: Track Irregularities Assessmentmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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Railways are one of the most important means of transportation, allowing people and goods to move quickly, environmentally beneficially, and efficiently over long distances. To ensure safe and reliable operations, regular condition-based assessments of trains and track are of paramount importance. Drive-by methodologies, which utilize data collected by onboard monitoring systems as the vehicle travels over the track, have gained popularity as an economically viable strategy for monitoring extensive track networks as well as vehicles traveling on them. This paper presents a critical review of these methodologies applied to railway tracks and vehicles. It assesses research on track irregularities, rail conditions, and the condition of rail supporting elements, highlighting important early developments and recent papers that provide insights into future practical applications. Additionally, the paper explores works related to global vehicle condition evaluation, focusing on the identification of suspension and wheelset element damage and also discussing challenges towards commercial application. The findings suggest that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. By leveraging drive-by assessments, railway operators can optimize maintenance efforts, detect faults early, predict remaining component life, evaluate track performance, monitor vehicle health, and make informed decisions based on data analysis. Finally, a comprehensive conclusion summarizes the achievements thus far and provides perspectives for forthcoming developments. The future practical applications of drive-by methodologies discussed in this review have the potential to revolutionize railway track and vehicle assessments, leading to safer and more efficient railway operations in the days to come.

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Section: Track Irregularities Assessmentmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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“…Although there are many ways to measure RTG, [3][4][5] the TGM systems used in all dynamic railway inspection vehicles in China are developed by our team. 6 The TGM system with existing fixed RPA parameters can only perform better detection of curve segments within a specific radius range and cannot adaptively adjust these parameters to perform high-quality detection of curve segments beyond the radius range.…”

Section: Introductionmentioning

confidence: 99%

Real-time railway plane alignment discrimination based on linear discriminant analysis of Lp norm with sparsity constraint

Qin

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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To improve the accuracy of detected parameters at curves by an in-use track geometry measurement (TGM) system, a real-time railway plane alignment (RPA) discrimination method based on linear discriminant analysis of an Lp norm with sparsity constraint (LDA-Lp-SC) is proposed. A standard database to identify the radius of a track curve is established, using the relevant ledger and track TGM data. Multi-dimensional feature extraction is performed on the TGM data, and the parameters of the method are optimized to improve the accuracy of the model classification. Finally, the method is embedded in a TGM system combined with the original RPA discrimination method, so that the key parameters of RPA discrimination can be automatically switched in real time according to the classification result of the track curve radius. Compared with the original method, the method in this study greatly improves the recognition accuracy of track curve with a radius in the range of 150–20,000 m, while ensuring real-time performance.

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“…Such issues could be overcome if the detection could proceed inside the vehicle cabin. To solve the problem, some research has emerged to infer track irregularity from vehicle body vibration (11)(12)(13). The traditional portable detector detects the vehicle body vibration in the vehicle cabin, and some researchers have used the body vibration detected by it to identify track irregularity (14).…”

mentioning

confidence: 99%

Identification of Subway Track Irregularities Based on Detection Data of Portable Detector

Liu

Wang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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At present, the detection of subway track irregularities is mainly carried out by track inspection vehicles and track inspection trolleys. Such detections are restricted by subway service time, so they can only be carried out once every few months. This study explored the possibility of using the vibration of the vehicle body collected by a novel portable detector to detect track geometry irregularities. It makes a particular contribution to the dynamic detection of track conditions and the reduction of maintenance costs. Based on the data collected by the portable detector, wavelet transform was used to analyze the vibration of the vehicle body. The results confirmed that this method was effective in enhancing the correlation between vibration accelerations and track irregularities. Second, a data set processed by wavelet transform was resampled by a hybrid sampling method which uses clustering methods and considers data imbalance within each category. In this way, the imbalance ratio of the data set was found to be reduced without changing the original data set structure. Finally, the random forest algorithm and the gradient boost decision tree algorithm were adopted for classifying track regularity and irregularity data. The results showed that both two algorithms, especially the random forest algorithm, performed well for the longitudinal level track irregularity and the alignment track irregularity.

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Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique

Cited by 16 publications

References 23 publications

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Real-time railway plane alignment discrimination based on linear discriminant analysis of Lp norm with sparsity constraint

Identification of Subway Track Irregularities Based on Detection Data of Portable Detector

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