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arXiv Machine Learning

Anomaly Detection and Localization for the Pantograph-Catenary System

The paper introduces a new framework for monitoring the Pantograph‑Catenary System (PCS) that localizes PCS height and stagger by aligning video‑derived measurements with nominal GPS coordinates of the reference route. It also presents a collective anomaly detection method to assess PCS health conditions. The authors evaluate both localization and detection on a real‑world dataset from an Italian railway company, covering multiple train journeys.

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arXiv AI
2d ago

Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

This survey reviews deep learning methods for detecting anomalies in railway systems, organizing them by taxonomy of anomaly location, data representation, sensing modality, and temporal traits. It categorizes approaches—convolutional, recurrent, attention-based, autoencoders, GANs, transformers—into classification, prediction, reconstruction, and hybrid paradigms, and discusses data challenges, evaluation, metrics, and deployment issues such as edge‑cloud architectures and hardware constraints. The paper also offers a decision‑oriented framework linking anomaly characteristics, data properties, and operational constraints to guide the selection and deployment of suitable detection solutions.

By Ammar Bouketta, Smail Niar, Hamza Ouarnoughi
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arXiv AI
Aug 11

An Explainable GNN Framework for Component-Level Anomaly Diagnosis

arXiv:2608. 09246v1 Announce Type: new Abstract: Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS).

By Sena Ozgunay (IMT, ANITI, LAAS-DISCO, LAAS, Comue de Toulouse), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Jean-Michel Loubes (IMT, REGALIA), Raul Sena Ferreira (LAAS)
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arXiv Machine Learning
Sep 3

TrajMind: Chaining Role-Specialized LoRAs for Fast-and-Slow Collective Trajectory Anomaly Diagnosis

TrajMind is a framework for diagnosing collective anomalies in urban trajectory data. It separates continuous screening from on-demand diagnosis, using a fast text-only path for alerts and a slow vision‑language path that chains role‑specialized LoRA adapters for detailed, evidence‑backed what‑who‑where‑when records. Experiments show the slow path outperforms baselines by over 15 percentage points in typing and 13 in localization, while the fast path cuts latency by 41% and retains high accuracy.

By Jiahao Wu, Zhenqun Yang, Chen Jason Zhang, Qing Li
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arXiv Machine Learning
Sep 22

Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach

The paper introduces Unsupervised Graph Collective Anomaly Detection (UGCAD), a framework that uses a variational graph autoencoder to learn graph representations of IoT network traffic and then enhances clustering to group nodes. UGCAD identifies collective anomalies by aggregating normal clusters and applying anomaly scores to the refined groups. Experiments on CICIoT2023 and ToN-IoT datasets show that UGCAD outperforms traditional and state‑of‑the‑art clustering‑based CAD methods in both clustering quality and anomaly detection accuracy.

By Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar, Youcef Djenouri
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arXiv Computer Vision
Sep 23

AT3D-AD: Anomaly Type-Aware 3D Anomaly Detection via Hierarchical Point-Language Alignment

AT3D-AD introduces a unified framework for detecting, localizing, and classifying 3D point‑cloud anomalies. It uses a Physics‑Driven Parametric Anomaly Synthesis module to generate synthetic defects for explicit supervision, a Hierarchical Global‑Local Anomaly Alignment module to refine representations, and a Semantic‑Geometric Anomaly Classification module to achieve precise, type‑discriminative localization. The method sets new state‑of‑the‑art results on four benchmarks, achieving high AUROC and Macro‑F1 scores.

By Jingyu Zeng, Haoquan Lu, Can Gao
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arXiv AI
Jun 4

TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection

arXiv:2606. 04073v1 Announce Type: cross Abstract: This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (\textbf{T}wo-stage \textbf{P}seudo \textbf{A}nomaly-guided \textbf{A}nomaly \textbf{D}etection, \textbf{TPA-AD}) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal samples are available for training.

By Xiancheng Wang, Zhibo Zhang, Ran Li, Rui Wang, Minghang Zhao, Shisheng Zhong, Lin Wang
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