An efficient approach for guided wave structural monitoring of switch rails via deep convolutional neural network-based transfer learning

Liu, Weixu; Tang, Zhifeng; Lv, Fuzai; Chen, Xiangxian

doi:10.1088/1361-6501/ac9ad3

Cited by 10 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…crack recognition [40]), informing preemptive, accounting for environmental effects [41], and recovery decisions by extracting patterns from data collected via various sources and media [42]. Intelligent diagnosis based on DL algorithms has received increasing attention and has achieved remarkable results in areas such as image classification, object detection, natural language processing, and image segmentation [43].…”

Section: Shm Techniquesmentioning

confidence: 99%

An overview of technological advancements in the application of structural health monitoring on glass elements

Ahani,

Zhang,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The concepts behind employing structural health monitoring (SHM) to the glass structures are in their early stages. While limited information by the application of SHM on glass-made elements could obtain through the literature, recent technological advances enrich the state of the art around the glass elements. As yet because of the novelty in both using SHM and the application of LGs for structural purposes, the historical background around the application of SHM on glass elements doesn't exceed 20 years, and the novelty of the concepts around the LGs may be even more. With this in mind, the demanded assessments have to be incept from the very basis of both the glass and SHM sciences to place the stepping stones of the fundamental stage, which would be a platform for the extending evaluations of glass elements at higher levels. This study is an endeavor to bring forward the available approaches for elaborating SHM on structural elements composed of glass with an emphasis on glass façades. According to the outcomes, the requirement for early detection due to its brittle content proffers structural health monitoring as the limited available alternatives for damage detection of the systems composed of it.

show abstract

Section: Shm Techniquesmentioning

confidence: 99%

An overview of technological advancements in the application of structural health monitoring on glass elements

Ahani,

Zhang,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Li et al [20] investigated the application of the modal curvature method and neural network technology to the often-neglected problem of track substrate defects, and they developed a new algorithm for detecting and quantifying track substrate defects, which was demonstrated through numerical simulations and experimental validation to show its effectiveness in both free and fixed-track inspection. Liu et al [21] proposed a deep convolutional neural-networkbased transfer learning (DCTL) approach to achieve effective damage identification in the health monitoring of sharp rail structures through affine transform data enhancement using a pre-trained Inception-ResNet-V2 model and a 1D signal-to-2D image conversion technique, and it showed higher performance than the traditional approach in the experiments. Zheng et al [22] introduced a deep-learning-based multi-object detection system for the non-destructive assessment of railway components employing an enhanced YOLOv5 for localization and Mask R-CNN for defect segmentation on rail surfaces, complemented by a ResNet framework for fastener classification.…”

Section: Introductionmentioning

confidence: 99%

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

Si,

Luo,

Han

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

show abstract

“…AI can be helpful in mapping various parameters of AE waves to the damage parameters such as location, severity and so forth. The application of ML and deep learning algorithms to solve complex problems in the domain of structural health monitoring and fault detection has been growing in recent years [16][17][18][19][20][21][22]. Ebrahimkhanlou et al [23] utilised deep stacked encoders to determine coordinates of AE sources in an aluminium plate observing 100% accuracy for zonal localisation.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach

Mahajan

Banerjee

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Acoustic emission (AE) approach for non-destructive evaluation of structure is being developed for last two decades. In complex structures, one of the limitation of AE testing is to find the location of AE source. Time of flight and wave velocity are typically employed to localise AE sources. However, complex rail structure generates multiple wave modes travelling at varying speeds, making localization a difficult task. In this paper, the challenge of localisation has been split up into two parts: (i) identification of AE source zone i.e. head, web and foot (ii) identification of location along length of rail. AE events are simulated using pencil lead break (PLB) as source. Three models including artificial neural network (ANN), 1D and 2D convolutional neural network (CNN) are trained and tested using AE signals generated by PLB sources. The accuracy of zone identification is reported as 94.79% by using 2D CNN algorithm. For location classification it is also found that 2D CNN performed best with 73.12%, 79.37% and 67.50% accuracy of localizing AE source along the length in head, web and foot, respectively. For AE signal generating from actual damage in rail, bending test on inverted damaged rail section was then performed with load cases of 100kN, 150kN and 200kN. For all load cases, 2D CNN model results in accurate prediction of the zone of AE source, whereas, it accurately predicts the AE source location along the length for the load cases of higher intensity (150kN, 200kN). It is envisaged that the deep learning approach presented in this research work is expected to be helpful in developing real time monitoring system for rail inspection based on AE.

show abstract

An efficient approach for guided wave structural monitoring of switch rails via deep convolutional neural network-based transfer learning

Cited by 10 publications

References 57 publications

An overview of technological advancements in the application of structural health monitoring on glass elements

An overview of technological advancements in the application of structural health monitoring on glass elements

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach

Contact Info

Product

Resources

About