Development of an Algorithm for Detecting Railway Corrugations in Acceleration Data

Belov, Ivan V.; Shalymov, Roman V.; Ткаченко, А. Н.; Larionov, Daniil Yu.; Podgornaya, L. N.

doi:10.1109/elconrus51938.2021.9396622

Cited by 8 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the regression model was used to compensate for and eliminate the influence of vehicles, and the expected amplitude of each feature of each box was obtained at the current speed. There are also studies on the classification of the rail-corrugation degree of the Vienna tramcar track, using a regression model to compensate, and using a machine-learning algorithm to test vibration acceleration characteristics, to eliminate the effect of rail corrugation on vehicles [49].…”

Section: Detection Methods For Rail Corrugation 41 Corrugation Detect...mentioning

confidence: 99%

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Wang,

Huang,

Wang

et al. 2024

Buildings

View full text Add to dashboard Cite

Rail corrugation is a serious problem in a railway transportation system, aggravating the operational risk and shortening the lifetime of train–track system. In order to ensure the safety and reliability of the railway system, the detection of rail corrugation is very important. Thus, this study systematically summarizes the recent research progress of rail corrugation. First, this study introduces the definition of rail corrugation and the classification criteria. Then, the formation mechanism of rail corrugation is analyzed in detail, and its adverse consequences are investigated. Further, this study summarizes several main detection methods, which are corrugation-detection methods based on acceleration measurements, wavelet transform methods for corrugation evaluation, computer-vision-based methods for corrugation automatic detection, digital filtering algorithms for rail corrugation detection, and others. In this study, the formation mechanism and detection methods of rail corrugation are systematically described, and various corrugation-detection methods are also introduced in detail. This study not only provides a scientific basis for railway maintenance, but also lays a solid foundation for future experimental design and data analysis. This study can also guide engineering practice to improve the reliability and safety of railway systems. It also provides useful experience for future railway-engineering design and planning, as well as safer and more reliable operation. In general, this study can provide technical support for the detection of rail corrugation to ensure the safety of the rail–track system.

show abstract

Section: Detection Methods For Rail Corrugation 41 Corrugation Detect...mentioning

confidence: 99%

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Wang,

Huang,

Wang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Balouchi et al [7] also reported that the dynamic responses in terms of axle box accelerations, under damage conditions, can be considered as the most reliable evidence of the extent of the damage to the track. Chang et al [33] applied a continuous wavelet transform (CWT) to detect the resonant frequency of the car body, using vertical and lateral acceleration registered at the floor level, under rail [34][35][36] and wheel wear damage conditions. Erduran et al [37] developed a methodology based on CWT for the detection of bridge vibration frequencies under track damage, using simulated bogie vibration signals, and found that the developed approach can detect the bridge vibration frequency with acceptable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Traquinho,

Vale,

Ribeiro

et al. 2023

Machines

View full text Add to dashboard Cite

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.

show abstract

Influence of shortwave irregularity on increasing the vehicle running speed on bi-block ballastless track lines

Zhao

Jin

et al. 2023

Construction and Building Materials

View full text Add to dashboard Cite

Development of an Algorithm for Detecting Railway Corrugations in Acceleration Data

Cited by 8 publications

References 3 publications

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Influence of shortwave irregularity on increasing the vehicle running speed on bi-block ballastless track lines

Contact Info

Product

Resources

About