The paper presents two complementary methods for detecting anomalies in real‑world bridge sensor data: a signal‑processing approach and a data‑driven Isolation Forest machine‑learning model. Real‑time data were collected from an iBridge sensor on a Norwegian bridge and evaluated through metrics such as anomaly counts, detection time, processing rate, and visualization. A controlled anomaly‑injection study showed distinct detection characteristics and computational demands for each method, underscoring the potential of combining machine learning with signal processing for bridge monitoring.
arXiv:2606. 05274v1 Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation.
arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...
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.
arXiv:2610.08822v1 Announce Type: new Abstract: Ageing bridge infrastructure is a growing global concern, yet conventional Structural Health Monitoring (SHM) systems are costly and difficult to scale...
arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.
arXiv:2607. 02046v1 Announce Type: new Abstract: Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems.
Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems. Traditionally, anomaly detection algorithms have been designed using both supervised and unsupervised learning paradigms.
arXiv:2606. 01691v1 Announce Type: cross Abstract: Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents.
arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.
arXiv:2610.35400v1 Announce Type: new Abstract: The rapid proliferation of Industrial Internet of Things devices has led to increasingly large-scale and non-stationary time-series data, posing signif...
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.
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.