接触网-受电弓系统中弧光检测的多模态学习
Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems
AI总结：
本文提出多模态学习框架，结合图像与力数据，有效检测接触网-受电弓系统中的弧光事件，提升检测鲁棒性和准确性。
摘要：
接触网-受电弓接口对于确保电气化铁路系统中不间断和可靠电力输送至关重要。然而，该接口处的电气弧光会造成严重风险，包括接触部件的加速磨损、系统性能下降以及可能的服务中断。检测接触网-受电弓接口处的弧光事件具有挑战性，因为它们具有瞬态性质，工作环境噪声大，数据稀缺，且难以将弧光与其他类似的瞬态现象区分开来。为了解决这些挑战，我们提出了一种新颖的多模态框架，结合高分辨率图像数据与力测量数据，以更准确和鲁棒地检测弧光事件。首先，我们构建了两个弧光检测数据集，包含同步的视觉和力测量数据。一个数据集来自瑞士联邦铁路（SBB）提供的数据，另一个则来源于不同铁路系统中公开的弧光事件视频和合成力数据，这些数据模拟了在真实数据集中观察到的特征。利用这些数据集，我们提出了MultiDeepSAD，这是DeepSAD算法的多模态扩展，具有新的损失公式。此外，我们引入了针对每种数据类型的定制伪异常生成技术，例如图像中的合成弧光伪影和模拟的力不规则性，以增强训练数据并提高模型的判别能力。通过广泛的实验和消融研究，我们证明了我们的框架在很大程度上优于基线方法，即使在域转移和真实弧光观测有限的情况下，也表现出对真实弧光事件的增强敏感性。
英文摘要：
The pantograph-catenary interface is essential for ensuring uninterrupted and reliable power delivery in electrified rail systems. However, electrical arcing at this interface poses serious risks, including accelerated wear of contact components, degraded system performance, and potential service disruptions. Detecting arcing events at the pantograph-catenary interface is challenging due to their transient nature, noisy operating environment, data scarcity, and the difficulty of distinguishing arcs from other similar transient phenomena. To address these challenges, we propose a novel multimodal framework that combines high-resolution image data with force measurements to more accurately and robustly detect arcing events. First, we construct two arcing detection datasets comprising synchronized visual and force measurements. One dataset is built from data provided by the Swiss Federal Railways (SBB), and the other is derived from publicly available videos of arcing events in different railway systems and synthetic force data that mimic the characteristics observed in the real dataset. Leveraging these datasets, we propose MultiDeepSAD, an extension of the DeepSAD algorithm for multiple modalities with a new loss formulation. Additionally, we introduce tailored pseudo-anomaly generation techniques specific to each data type, such as synthetic arc-like artifacts in images and simulated force irregularities, to augment training data and improve the discriminative ability of the model. Through extensive experiments and ablation studies, we demonstrate that our framework significantly outperforms baseline approaches, exhibiting enhanced sensitivity to real arcing events even under domain shifts and limited availability of real arcing observations.
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