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Fault Detection, Diagnosis and Maintenance on Intelligent Transportation System

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

Deadline for manuscript submissions: 31 March 2024 | Viewed by 2811

Special Issue Editors

Department of Built Environment, Oslo Metropolitan University, Pilestredet 35, 0166 Oslo, Norway
Interests: fault detection; performance assessment; structural analysis and optimisation of train-track system; wind-infrastructure & pantograph-catenary interaction
Special Issues, Collections and Topics in MDPI journals
School of Civil Engineering Central South University, Central South University, Changsha 410083, China
Interests: design, analysis, and application of nonlinear vibration absorber; suppression of wind-induced vibrations in long-span bridges; analysis and control of human-induced vibrations in slender and flexible footbridges; smart construction and maintenance of bridges using artificial intelligence techniques
Department of Built Environment (DBE), Faculty of Technology, Arts and Design (TKD), Oslo Metropolitan University, Pilestredet 35, 0166 Oslo, Norway
Interests: civil engineering; structural engineering; digital twins; structural health monitoring; cultural heritage conservation; bridges

Special Issue Information

Dear Colleagues,

The monitoring and management of traffic structures is an important part of the modern traffic system. Modern and efficient road/rail infrastructure operations require regular monitoring and timely maintenance. By their nature, these are multidisciplinary challenges that require collaboration among different disciplines to develop and implement innovative solutions. In recent years, the development of sensing, the Internet of Things, big data, artificial intelligence, digitalization, visualization, and digital twins have provided novel solutions for the fault detection and diagnosis of existing transportation structures, which has ultimately led to improved management, sustainability, and extended life of such key infrastructure assets. This Special Issue aims to provide a unique platform to publish state-of-the-art methods and applications for monitoring traffic structures. Both original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Digital twin
  • Structural health monitoring
  • Vehicle-infrastructure interaction
  • Bridge engineering
  • Tunnel engineering
  • Active monitoring in structural engineering
  • Smart railway
  • Vision-based structural fault diagnosis

Dr. Yang Song
Prof. Dr. Xiaojun Wei
Dr. Alejandro Jimenez Rios
Guest Editors

Manuscript Submission Information

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Published Papers (4 papers)

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Research

18 pages, 4831 KiB  
Article
Suppression of Railway Catenary Galloping Based on Structural Parameters’ Optimization
Sensors 2024, 24(3), 976; https://doi.org/10.3390/s24030976 - 02 Feb 2024
Viewed by 350
Abstract
Railway catenary galloping, induced by aerodynamic instability, poses a significant threat by disrupting the electric current connection through sliding contact with the contact wire. This disruption leads to prolonged rail service interruptions and damage to the catenary’s suspension components. This paper delves into [...] Read more.
Railway catenary galloping, induced by aerodynamic instability, poses a significant threat by disrupting the electric current connection through sliding contact with the contact wire. This disruption leads to prolonged rail service interruptions and damage to the catenary’s suspension components. This paper delves into the exploration of optimizing the catenary system’s structure to alleviate galloping responses, addressing crucial parameters such as span length, stagger dropper distribution, and tension levels. Employing a finite element model, the study conducts simulations to analyze the dynamic response of catenary galloping, manipulating structural parameters within specified ranges. To ensure accurate and comprehensive exploration, the Sobol sequence is utilized to generate low-discrepancy, quasi-random, and super-uniform distribution sequences for the high-dimensional parameter inputs. Subsequent to the simulation phase, a genetic algorithm based on neural networks is employed to identify optimal parameter settings for suppressing catenary galloping, taking into account various constraints. The results gleaned from this investigation affirm that adjusting structural parameters can effectively diminish the galloping amplitude of the railway catenary. The most impactful strategy involves augmenting tension and reducing span length. Moreover, even when tension and span length are fixed, adjusting other parameters demonstrates efficacy in reducing galloping amplitudes. The adjustment of messenger-wire tension, dropper distribution, and stagger can achieve a 22.69% reduction in the maximum vertical galloping amplitude. Notably, maintaining a moderate stagger value and a short steady arm–dropper distance is recommended to achieve the minimum galloping amplitude. This research contributes valuable insights into the optimization of railway catenary systems, offering practical solutions to mitigate galloping-related challenges and enhance overall system reliability. Full article
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23 pages, 8380 KiB  
Article
Residual Shrinkage ViT with Discriminative Rebalancing Strategy for Small and Imbalanced Fault Diagnosis
Sensors 2024, 24(3), 890; https://doi.org/10.3390/s24030890 - 30 Jan 2024
Viewed by 379
Abstract
In response to the challenge of small and imbalanced Datasets, where the total Sample size is limited and healthy Samples significantly outweigh faulty ones, we propose a diagnostic framework designed to tackle Class imbalance, denoted as the Dual-Stream Adaptive Deep Residual Shrinkage Vision [...] Read more.
In response to the challenge of small and imbalanced Datasets, where the total Sample size is limited and healthy Samples significantly outweigh faulty ones, we propose a diagnostic framework designed to tackle Class imbalance, denoted as the Dual-Stream Adaptive Deep Residual Shrinkage Vision Transformer with Interclass–Intraclass Rebalancing Loss (DSADRSViT-IIRL). Firstly, to address the issue of limited Sample quantity, we incorporated the Dual-Stream Adaptive Deep Residual Shrinkage Block (DSA-DRSB) into the Vision Transformer (ViT) architecture, creating a DSA-DRSB that adaptively removes redundant signal information based on the input data characteristics. This enhancement enables the model to focus on the Global receptive field while capturing crucial local fault discrimination features from the extremely limited Samples. Furthermore, to tackle the problem of a significant Class imbalance in long-tailed Datasets, we designed an Interclass–Intraclass Rebalancing Loss (IIRL), which decouples the contributions of the Intraclass and Interclass Samples during training, thus promoting the stable convergence of the model. Finally, we conducted experiments on the Laboratory and CWRU bearing Datasets, validating the superiority of the DSADRSViT-IIRL algorithm in handling Class imbalance within mixed-load Datasets. Full article
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18 pages, 4106 KiB  
Article
A Fault Detection Approach Based on One-Sided Domain Adaptation and Generative Adversarial Networks for Railway Door Systems
Sensors 2023, 23(24), 9688; https://doi.org/10.3390/s23249688 - 07 Dec 2023
Viewed by 524
Abstract
Fault detection using the domain adaptation technique is one of the more promising methods of solving the domain shift problem, and has therefore been intensively investigated in recent years. However, the domain adaptation method still has elements of impracticality: firstly, domain-specific decision boundaries [...] Read more.
Fault detection using the domain adaptation technique is one of the more promising methods of solving the domain shift problem, and has therefore been intensively investigated in recent years. However, the domain adaptation method still has elements of impracticality: firstly, domain-specific decision boundaries are not taken into consideration, which often results in poor performance near the class boundary; and secondly, information on the source domain needs to be exploited with priority over information on the target domain, as the source domain can provide a rich dataset. Thus, the real-world implementations of this approach are still scarce. In order to address these issues, a novel fault detection approach based on one-sided domain adaptation for real-world railway door systems is proposed. An anomaly detector created using label-rich source domain data is used to generate distinctive source latent features, and the target domain features are then aligned toward the source latent features in a one-sided way. The performance and sensitivity analyses show that the proposed method is more accurate than alternative methods, with an F1 score of 97.9%, and is the most robust against variation in the input features. The proposed method also bridges the gap between theoretical domain adaptation research and tangible industrial applications. Furthermore, the proposed approach can be applied to conventional railway components and various electro-mechanical actuators. This is because the motor current signals used in this study are primarily obtained from the controller or motor drive, which eliminates the need for extra sensors. Full article
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22 pages, 14960 KiB  
Article
Modelling and Analysis of Expansion Joints’ Effect on Dynamic Performance of Railway Rigid Overhead System
Sensors 2023, 23(15), 6797; https://doi.org/10.3390/s23156797 - 29 Jul 2023
Viewed by 922
Abstract
This study focuses on developing a comprehensive model of a rigid overhead system, which includes essential components such as the suspension structure, positioning clamp, and expansion joint. The modelling approach utilizes finite element theory and beam elements to accurately represent the displacement, stiffness, [...] Read more.
This study focuses on developing a comprehensive model of a rigid overhead system, which includes essential components such as the suspension structure, positioning clamp, and expansion joint. The modelling approach utilizes finite element theory and beam elements to accurately represent the displacement, stiffness, and mass characteristics of the system. The models also incorporate the suspension structure and positioning line clamp, which play crucial roles in suspending and positioning the busbar. Various suspension structures and positioning line clamps are evaluated based on their dynamic characteristics. The expansion joint, responsible for connecting different anchor sections of the rigid overhead system, undergoes a detailed analysis. Different assembly scenarios, including ideal and deflected assembly conditions, are considered. To simulate the dynamic behaviour of the expansion joint, additional beams are introduced into the system model. The primary finding of the analysis is that the maximum stresses observed in the constructed expansion joint model, under different temperature conditions and normal/deflected assembly conditions, remain within the permissible stress limits of the material. This indicates a high level of safety. However, certain areas exhibit stress concentration, particularly at the sliding block B and sliding rod A positions. This stress concentration is primarily attributed to the unique assembly form of the expansion joint. To improve stress distribution and enhance service reliability, the analysis suggests optimizing the installation deflection angle and geometric design of the expansion joint. Furthermore, the concentrated mass at the expansion joint significantly impacts the current collection quality of the pantograph-overhead system. Mitigating this negative impact can be achieved by reducing the mass of the expansion joint. Full article
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