VDTformer:一种基于变压器的框架,用于电缆隧道的精确变形风险检测

IF 4.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2025-06-15 DOI:10.1016/j.array.2025.100422
Ruipeng Liu , Jieyan Zhang, Pengfei Chen, Yunxun Liu, Wanlin Quan, Junliang Su
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引用次数: 0

摘要

电缆隧道是城市电力系统的重要组成部分,保证了高压电力的可靠传输。然而,由于地质和环境因素的影响,其结构完整性受到冠状沉降和墙体位移等变形风险的威胁。目前使用分布式光纤传感器的监测方法面临着巨大的挑战,因为采集到的振动信号具有高噪声和非平稳性,这阻碍了准确的风险检测。在这项工作中,我们提出了VDTformer,这是一个基于变压器的框架,它集成了用于去噪和特征提取的滤波器组卷积(FBC)模块和用于捕获非平稳特征的基于小波变换的注意力(WTA)机制。在真实世界数据上的大量实验表明,我们的方法显著提高了检测精度和鲁棒性,达到95.8%的准确率和95.5%的Macro-F1分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VDTformer: A transformer-based framework for accurate deformation risk detection in cable tunnels
Cable tunnels are critical components of urban power systems, ensuring reliable transmission of high-voltage electricity. However, their structural integrity is threatened by deformation risks, such as crown settlement and wall displacement, due to geological and environmental factors. Current monitoring methods using distributed optical fiber sensors face significant challenges because the acquired vibration signals are highly noisy and non-stationary, which hampers accurate risk detection. In this work, we propose VDTformer, a Transformer-based framework that integrates a Filter Bank Convolution (FBC) module for denoising and feature extraction with a Wavelet Transform-based Attention (WTA) mechanism for capturing non-stationary characteristics. Extensive experiments on real-world data demonstrate that our approach significantly improves detection accuracy and robustness over conventional methods, achieving an accuracy of 95.8% and a Macro-F1 score of 95.5%.
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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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