Track Defect Detection for High-Speed Maglev Trains via Deep Learning

He, Yongxiang; Wu, Jun; Zheng, Yaojia; Zhang, Yuxin; Hong, Xiaobo

doi:10.1109/tim.2022.3151165

Cited by 19 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Offering a blend of efficiency, environmental sustainability, and unparalleled connectivity, rail transportation has not only bridged distant geographies but has also fostered economic growth, mitigated urban congestion, and introduced a greener mode of transit. Consequently, the study of such systems has received significant attention, leading to numerous research findings [1][2][3] .…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode-Based Distributed Trajectory Tracking Control of Four-body Train Systems

Ding,

Yan

2024

j. of electron. & inf. syst.

View full text Add to dashboard Cite

This paper considers the speed tracking of a four-body train system modelled mathematically based on Newton’s second law, which is described by a large-scale interconnected system with four subsystems. Uncertainties are included in the systems to represent the potential impacts on system performance caused by mechanical wear and external environmental changes. An adaptive sliding mode technique is employed to design a distributed control scheme to guarantee tracking accuracy. Coordinate transformations are introduced to transfer the model of train systems to a system in regular form to facilitate the design of the hyperplane and controllers. The Barbashin-Krasovskii theorem is employed to show the reachability of the hyperplane. In simulations, the Gaussian function is chosen as the desired signal, representing time-varying characteristics relevant to real-world situations, and the result demonstrates the feasibility of the proposed control strategy.

show abstract

Section: Introductionmentioning

confidence: 99%

Sliding Mode-Based Distributed Trajectory Tracking Control of Four-body Train Systems

Ding,

Yan

2024

j. of electron. & inf. syst.

View full text Add to dashboard Cite

show abstract

“…As a typical green, safe and efficient mode of transportation, maglev rail transit has great potential for development [1]. Magnetic levitation, as an advanced technology for rail transportation, will promote further development and application in transportation [2,3]. At present, China, the US, Germany and Japan are the four most developed countries in maglev technology, and are clearly leading in the international arena with abundant research results [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

show abstract

“…As a result, deep-learningbased anomaly detection methods have gained traction in railway detection due to their inherent characteristics of speed, nondestructiveness, and high precision [6,7]. Within the domain of high-speed railway, abnormal detection can be categorized into three main approaches: unsupervised methods [8], object detection methods [9][10][11], and defect segmentation methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

Normalizing Flow‐Based Industrial Complex Background Anomaly Detection

Wen,

Gao,

Wang

et al. 2023

Journal of Sensors

View full text Add to dashboard Cite

This paper proposes a novel approach called cross-scale with attention normalizing flow (CSA-Flow) enhanced with channel-attention (CA) and self-attention (SA) modules for high-speed railway anomaly detection in complex industrial backgrounds to reduce the manual workload of the primary maintenance of high-speed electric multiple units. Detecting defects in industrial environments, characterized by intricate backgrounds and unclear subjects, poses significant challenges. To address this, CSA-Flow introduces a channel feature extraction module that combining the pretrained convolutional neural network models with a CA module for feature extraction, capturing information at different scales, and uses the SA module to capture more contextual information by its larger receptive field. The performance evaluation of CSA-Flow on the MVTec-AD dataset demonstrates an impressive area under the receiver operating characteristic curve (AUROC) score of 98.7%, with an equally remarkable score of 98.4% across all object classes. To further assess the effectiveness of CSA-Flow in complex background scenarios, we introduce a dedicated dataset, specifically designed for high-speed rail braking devices (HSRBDs). The experimental results establish the superiority of CSA-Flow over current state-of-the-art approaches in terms of both AUROC score and recall score, validating its exceptional capability for detecting anomalies in industrial complex backgrounds.

show abstract

Track Defect Detection for High-Speed Maglev Trains via Deep Learning

Cited by 19 publications

References 28 publications

Sliding Mode-Based Distributed Trajectory Tracking Control of Four-body Train Systems

Sliding Mode-Based Distributed Trajectory Tracking Control of Four-body Train Systems

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Normalizing Flow‐Based Industrial Complex Background Anomaly Detection

Contact Info

Product

Resources

About