arXiv Machine Learning

Generative multi-domain transfer learning for fault detection in data-scarce wind turbines

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 Machine Learning

3d ago

Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data

arXiv:2611.06194v1 Announce Type: new Abstract: Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few asse...

By Zekai Wang, Yuxuan Li, Zhangyue Shi

fine-tuning safety

arXiv Machine Learning

Jul 7

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.

By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva

safety

arXiv AI

Jul 17

VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection

arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.

By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das

llms multimodal benchmarks

arXiv AI

Oct 28

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems

The paper evaluates Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models for unsupervised anomaly detection in federated predictive maintenance. It compares full and partial federation setups, showing that full federation improves GAN performance but VAE and DDPM models are more robust. Decoding‑only sharing in Diffusion Models achieves similar utility to full federation while cutting communication costs.

By Usevalad Milasheuski, Piero Baraldi, Enrico Zio, Stefano Savazzi

diffusion benchmarks safety

arXiv AI

Jul 2

Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

arXiv:2607. 00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge.

By Seung Hun Han, Hyeongwon Kang, Jinwoo Park, Pilsung Kang

arXiv AI

Jun 26

Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines

arXiv:2606. 26710v1 Announce Type: new Abstract: Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact.

By Mohammed Ayalew Belay, Lucas Ferreira Bernardino, Adil Rasheed, Rub\'en M. Monta\~n\'es, Pierluigi Salvo Rossi

rag

arXiv Machine Learning

3d ago

Graph Foundation Models Can Become Effective Multivariate Time Series Anomaly Detectors

arXiv:2611.06441v1 Announce Type: new Abstract: Multivariate time series anomaly detection (MTSAD) relies on modeling both temporal dynamics and inter-sensor dependencies to identify complex system f...

By Zhuangyan Hu, Zhuoxuan Liang, Wei Li, Xiaohua Jia, Moustafa Youssef, Sumi Helal

multimodal safety

arXiv Machine Learning

Oct 15

Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection

arXiv:2604.17998v2 Announce Type: replace Abstract: Anomaly detection in multivariate time series is a central challenge in industrial monitoring, as failures frequently arise from complex temporal d...

By Pooyan Khosravinia, Jo\~ao Gama, Bruno Veloso

llms benchmarks safety

arXiv Machine Learning

Sep 24

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.

By Haochen Zhang, Jie Peng, Songyuan Sui, Yu-Chao Huang, Xiangqi Zhu, Tianlong Chen

fine-tuning benchmarks safety

arXiv AI

Oct 15

InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection

arXiv:2610.15032v1 Announce Type: cross Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in whi...

By {\L}ukasz Marcjan, Dorota Wilk-Ko{\l}odziejczyk, Roberto Corizzo

benchmarks

arXiv Machine Learning

Jun 17

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604. 17616v3 Announce Type: replace Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems.

By Shashank Mishra, Karan Patil, Cedric Schockaert, Didier Stricker, Jason Rambach

rag benchmarks

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