arXiv:2607. 23486v1 Announce Type: new Abstract: Dissolved gas analysis (DGA) is crucial for diagnosing early power transformer failures.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2604. 28118v2 Announce Type: replace-cross Abstract: Transformers now underpin critical AI systems across industry and research.
arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.
arXiv:2607. 29657v1 Announce Type: new Abstract: Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness.
arXiv:2607. 27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design.
arXiv:2608. 13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation.
arXiv:2607. 15705v1 Announce Type: new Abstract: Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems.