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Automation in Non-destructive Testing (NDT) Technology for Real-Time Railway Infrastructure Fatigue Diagnosis and Prognosis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 10777

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Special Issue Editors

Dr. Yunlong Guo

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Faculty of Civil Engineering and Geoscience, Delft University of Technology, 2628 CN Delft, The Netherlands
Interests: railway track structural health monitoring; ground penetrating radar for ballast layer inspection; track geometry inspection; track structure optimization; sleeper design; ballast layer design; railway circularity; waste tyre reuse; recycled ballast reuse; smart railway maintenance
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guoqing Jing

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Guest Editor

School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: railway engineering training; track structure; railway ballast; composite sleeper; LCC
Special Issues, Collections and Topics in MDPI journals

Dr. Sakdirat Kaewunruen

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Guest Editor

Birmingham Centre for Railway Research and Education, The University of Birmingham, Edgbaston B152TT, UK
Interests: railway infrastructure; circular economy; resilience; sustainability; digitalisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The EU Sustainable and Smart Mobility Strategy states that “Digitalisation will become an indispensable driver for the modernization of the entire system, making it seamless and more efficient. Europe also needs to use digitalization and automation to further increase the levels of safety, security, reliability, and comfort…”. However, the digitalization of railway infrastructure has not been developed thoroughly. Stemming from state-of-the-art NDT technologies for field railway infrastructure inspection, automatically processing and analysing the inspection data will be the next step toward real-time infrastructure fatigue diagnosis and prognosis. This Special Issue aims to provide selected contributions on advances in automation, fatigue/defect diagnosis and prognosis as well as new NDT technology development for railway infrastructure.

Potential topics include, but are not limited to:

AI application in NDT inspection data processing;
Digital twins in railway infrastructure;
Advanced numerical modelling using NDT inspection data;
Predictive maintenance for railway infrastructure using NDT technology;
NDT technology for railway circularity;
Sustainable preventative maintenance using NDT technology.

Dr. Yunlong Guo
Prof. Dr. Guoqing Jing
Dr. Sakdirat Kaewunruen
Guest Editors

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Keywords

AI
machine learning
digital twin
railway infrastructure
NDT technology
GPR
LiDAR

Published Papers (4 papers)

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Research

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18 pages, 9503 KiB

Open AccessArticle

Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels

by Ziren Yin, Zhanzhan Lei, Ao Zheng, Jiasong Zhu and Xiao-Zhou Liu

Sensors 2023, 23(16), 7106; https://doi.org/10.3390/s23167106 - 11 Aug 2023

Cited by 2 | Viewed by 747

Abstract

The surface defects on a shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise operation safety. To effectively detect multiple surface defects, this study uses a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting an inspection of the shield tunnel on a metro line section, various surface defects are identified with the TIT, including water leakage defects, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, association rules between different defects are calculated using an improved Apriori algorithm. The results show that: (i) there are significant differences in different association rules for various surface defects on the shield tunnel; (ii) the average confidence of the association rule “dislocation & spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; (iii) the weakest rule appears at “water leakage → spalling”, with an average confidence of 13%. The association analysis can be used for predicting the critical defects influencing structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for a shield subway tunnel. Full article

(This article belongs to the Special Issue Automation in Non-destructive Testing (NDT) Technology for Real-Time Railway Infrastructure Fatigue Diagnosis and Prognosis)

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22 pages, 4429 KiB

Open AccessArticle

Digital Twins for Managing Railway Bridge Maintenance, Resilience, and Climate Change Adaptation

by Sakdirat Kaewunruen, Mohannad AbdelHadi, Manwika Kongpuang, Withit Pansuk and Alex M. Remennikov

Sensors 2023, 23(1), 252; https://doi.org/10.3390/s23010252 - 26 Dec 2022

Cited by 24 | Viewed by 5473

Abstract

Innovative digital twins (DTs) that allow engineers to visualise, share information, and monitor the condition during operation is necessary to optimise railway construction and maintenance. Building Information Modelling (BIM) is an approach for creating and managing an inventive 3D model simulating digital information that is useful to project management, monitoring and operation of a specific asset during the whole life cycle assessment (LCA). BIM application can help to provide an efficient cost management and time schedule and reduce the project delivery time throughout the whole life cycle of the project. In this study, an innovative DT has been developed using BIM integration through a life cycle analysis. Minnamurra Railway Bridge (MRB), Australia, has been chosen as a real-world use case to demonstrate the extended application of BIM (i.e., the DT) to enhance the operation, maintenance and asset management to improve the sustainability and resilience of the railway bridge. Moreover, the DT has been exploited to determine GHG emissions and cost consumption through the integration of BIM. This study demonstrates the feasibility of DT technology for railway maintenance and resilience optimisation. It also generates a virtual collaboration for co-simulations and co-creation of values across stakeholders participating in construction, operation and maintenance, and enhancing a reduction in costs and GHG emission. Full article

(This article belongs to the Special Issue Automation in Non-destructive Testing (NDT) Technology for Real-Time Railway Infrastructure Fatigue Diagnosis and Prognosis)

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16 pages, 5925 KiB

Open AccessArticle

Differential Deformation Identification of High-Speed Railway Substructures Based on Dynamic Inspection of Longitudinal Level

by Shuai Ma, Xiubo Liu, Bo Zhang and Jianmei Wei

Sensors 2023, 23(1), 219; https://doi.org/10.3390/s23010219 - 25 Dec 2022

Viewed by 1448

Abstract

High-speed railway administrations are particularly concerned about safety and comfort issues, which are sometimes threatened by the differential deformation of substructures. Existing deformation-monitoring techniques are impractical for covering the whole range of a railway line at acceptable costs. Fortunately, the information about differential substructure deformation is contained in the dynamic inspection data of longitudinal level from comprehensive inspections trains. In order to detect potential differential deformations, an identification method, combining digital filtering, a convolutional neural network and infrastructure base information, is proposed. In this method, a low-pass filter is designed to remove short-waveband components of the longitudinal level. Then, a one-dimensional convolutional neural network is constructed to serve as a feature extractor from local longitudinal-level waveforms, and a binary classifier of potential differential deformations in place of the visual judgement of humans with profound expertise. Finally, the infrastructure base information is utilized to further classify the differential deformations into several types, according to the positional distribution of the substructures. The inspection data of four typical high-speed railways are selected to train and test the method. The results show that the convolutional neural network can identify differential substructure-deformations, with the precision, recall, accuracy and F1 score all exceeding 98% on the test data. In addition, four types of deformation can be further classified with the support of infrastructure base information. The proposed method can be used for directly locating adverse substructure deformations, and is also becoming a promising addition to existing deformation monitoring methods. Full article

(This article belongs to the Special Issue Automation in Non-destructive Testing (NDT) Technology for Real-Time Railway Infrastructure Fatigue Diagnosis and Prognosis)

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Review

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20 pages, 7632 KiB

Open AccessReview

A Review of NDT Methods for Wheel Burn Detection on Rails

by Yanbo Zhang, Xiubo Liu, Longhui Xiong, Zhuo Chen and Jianmei Wei

Sensors 2023, 23(11), 5240; https://doi.org/10.3390/s23115240 - 31 May 2023

Cited by 2 | Viewed by 1697

Abstract

Wheel burn can affect the wheel–rail contact state and ride quality. With long-term operation, it can cause rail head spalling or transverse cracking, which will lead to rail breakage. By analyzing the relevant literature on wheel burn, this paper reviews the characteristics, mechanism of formation, crack extension, and NDT methods of wheel burn. The results are as follows: Thermal-induced, plastic-deformation-induced, and thermomechanical-induced mechanisms have been proposed by researchers; among them, the thermomechanical-induced wheel burn mechanism is more probable and convincing. Initially, the wheel burns appear as an elliptical or strip-shaped white etching layer with or without deformation on the running surface of the rails. In the latter stages of development, this may cause cracks, spalling, etc. Magnetic Flux Leakage Testing, Magnetic Barkhausen Noise Testing, Eddy Current Testing, Acoustic Emission Testing, and Infrared Thermography Testing can identify the white etching layer, and surface and near-surface cracks. Automatic Visual Testing can detect the white etching layer, surface cracks, spalling, and indentation, but cannot detect the depth of rail defects. Axle Box Acceleration Measurement can be used to detect severe wheel burn with deformation. Full article

(This article belongs to the Special Issue Automation in Non-destructive Testing (NDT) Technology for Real-Time Railway Infrastructure Fatigue Diagnosis and Prognosis)

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Automation in Non-destructive Testing (NDT) Technology for Real-Time Railway Infrastructure Fatigue Diagnosis and Prognosis

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