Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems

• URL: http://arxiv.org/abs/2602.08792v1
• Date: Mon, 09 Feb 2026 15:29:19 GMT
• Title: Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems
• Authors: Hao Dong, Eleni Chatzi, Olga Fink,
• Abstract summary: arcing at the pantograph-catenary interface poses serious risks to power delivery in electrified rail systems.<n>We propose a novel framework that combines high-resolution image data with force measurements to more accurately and robustly detect arcing events.<n>Our framework significantly outperforms baseline approaches, exhibiting enhanced sensitivity to real arcing events even under domain shifts.
• Score: 23.447865285347504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond [2.4449457537548036]
  Anomaly detection in complex, high-dimensional data, such as UAV sensor readings, is essential for operational safety.<n>We propose the Diffuse to Detect (DTD) framework, a novel approach that adapts diffusion models for anomaly detection.<n>DTD employs a single-step diffusion process to predict noise patterns, enabling rapid and precise identification of anomalies without reconstruction errors.
  arXiv  Detail & Related papers  (2025-10-27T02:08:08Z)
• FSMODNet: A Closer Look at Few-Shot Detection in Multispectral Data [7.459632891054827]
  Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal data.<n>We introduce a framework named "FSMODNet" that leverages cross-modality feature integration to improve detection performance even with limited labels.
  arXiv  Detail & Related papers  (2025-09-25T08:45:05Z)
• SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations [68.9300049150948]
  We address a fundamental challenge in Reinforcement Learning from Interaction Demonstration (RLID)<n>Existing data collection approaches yield sparse, disconnected, and noisy trajectories that fail to capture the full spectrum of possible skill variations and transitions.<n>We present two data augmentation techniques: a Stitched Trajectory Graph (STG) that discovers potential transitions between demonstration skills, and a State Transition Field (STF) that establishes unique connections for arbitrary states within the demonstration neighborhood.
  arXiv  Detail & Related papers  (2025-05-04T13:00:29Z)
• Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data [0.0]
  This study proposes an anomaly causal discovery approach (AnomalyCD)<n>It addresses the accuracy and computational challenges of generating causal graphs from binary flag data sets.<n>Results demonstrate the considerable reduction of the computation overhead and moderate enhancement of the accuracy of temporal causal discovery.
  arXiv  Detail & Related papers  (2024-12-16T14:11:28Z)
• Leveraging Mixture of Experts for Improved Speech Deepfake Detection [53.69740463004446]
  Speech deepfakes pose a significant threat to personal security and content authenticity. We introduce a novel approach for enhancing speech deepfake detection performance using a Mixture of Experts architecture.
  arXiv  Detail & Related papers  (2024-09-24T13:24:03Z)
• DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition [51.96660522869841]
  DailyDVS-200 is a benchmark dataset tailored for the event-based action recognition community. It covers 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. DailyDVS-200 is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions.
  arXiv  Detail & Related papers  (2024-07-06T15:25:10Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Multi-dataset Training of Transformers for Robust Action Recognition [75.5695991766902]
  We study the task of robust feature representations, aiming to generalize well on multiple datasets for action recognition. Here, we propose a novel multi-dataset training paradigm, MultiTrain, with the design of two new loss terms, namely informative loss and projection loss. We verify the effectiveness of our method on five challenging datasets, Kinetics-400, Kinetics-700, Moments-in-Time, Activitynet and Something-something-v2.
  arXiv  Detail & Related papers  (2022-09-26T01:30:43Z)
• Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics [77.13542705329328]
  We propose a framework of disentangled representation learning(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning. The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics. Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments.
  arXiv  Detail & Related papers  (2022-08-04T15:46:48Z)
• Federated Variational Learning for Anomaly Detection in Multivariate Time Series [13.328883578980237]
  We propose an unsupervised time series anomaly detection framework in a federated fashion. We leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models.
  arXiv  Detail & Related papers  (2021-08-18T22:23:15Z)
• Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning [3.0100975935933567]
  We present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset.
  arXiv  Detail & Related papers  (2020-11-12T10:15:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.