arXiv Machine Learning June 16, 2026 By Eduardo Jr Piedad, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt

Early Anomaly-Onset Detection based on Wigner--Ville Distribution Slice Spectra: A Transmission-Grid Test Case

Read the original on arXiv Machine Learning →

arXiv:2606. 15856v1 Announce Type: cross Abstract: Operational disturbance monitoring in power networks requires decisions to be made from waveform windows as they arrive, rather than from completed records after the event.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 3

Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data

arXiv:2606. 03112v1 Announce Type: cross Abstract: With the increasing scale and number of wind farms, wind turbines' daily operation and maintenance costs are increasing.

By Jingzhe Kang

llms diffusion multimodal benchmarks safety

arXiv AI

Jun 9

PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection

arXiv:2606. 08935v1 Announce Type: cross Abstract: Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks.

By Kang Zhang, Wei Jian Lau, Shoushou Ren, Dong Lin, Joon Son Chung, Chuanhao Sun

rag benchmarks

arXiv AI

Jun 11

Reliability-Calibrated Edge-IoT Early Fault Warning for Rotating Machinery with a Physics-Guided Tiny-Mamba Transformer

arXiv:2601. 21293v3 Announce Type: replace-cross Abstract: Industrial Internet of Things (IIoT) systems increasingly rely on distributed vibration sensing to support predictive maintenance of rotating machinery.

By Changyu Li, Huabei Nie, Xiaoya Ni, Lu Wang, Lijuan Shen, Kaishun Wu, Fei Luo

llms

arXiv AI

Jun 3

Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

arXiv:2606. 02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels.

By Marc Pinet (LIG), Julien Cumin (LIG), Samuel Berlemont (LIG), Dominique Vaufreydaz (LIG)

benchmarks

arXiv Machine Learning

Sep 15

Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals

arXiv:2505.17763v2 Announce Type: replace Abstract: The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially d...

By Julian Oelhaf, Georg Kordowich, Andreas Maier, Johann J\"ager, Siming Bayer

safety

arXiv AI

3d ago

Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection

The paper introduces the Temporal Cycle-Aware Attention Autoencoder with Cross-Signal Consistency (TCAA‑CS) for detecting anomalies in railway passenger door operations. It treats each full opening‑dwell‑closing cycle as a monitoring unit and trains on nominal cycles using a dual‑stream encoder for physical measurements and logical states, an LSTM with temporal attention, and a hybrid anomaly score that fuses reconstruction error, latent‑space deviation, and phase‑aware cross‑signal consistency. On real industrial data, TCAA‑CS achieves 93.8% recall, 97.3% precision, and a 0.5% false‑alarm rate, outperforming other unsupervised baselines and demonstrating real‑time feasibility on an NVIDIA Jetson AGX Xavier.

By Ammar Bouketta, Smail Niar, Hamza Ouarnoughi, Eva Mutuzo Brindle

safety

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Trans GAN-WT: A Feature Extraction and Interactive Learning-Based Anomaly Detection Model for Wind Turbine Time Series Data

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