arXiv Machine Learning
Generative multi-domain transfer learning for fault detection in data-scarce wind turbines
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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.
arXiv Machine Learning
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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...
arXiv Machine Learning
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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.
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.
arXiv AI
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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.
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.