arXiv AI
Jun 26
Hierarchical Fault Detection and Diagnosis for Transformer Architectures
arXiv:2604. 28118v2 Announce Type: replace-cross Abstract: Transformers now underpin critical AI systems across industry and research.
An adaptive multi-fuzzy logic model for diagnosing transformer faults using dynamic weight optimization
arXiv:2607. 23486v1 Announce Type: new Abstract: Dissolved gas analysis (DGA) is crucial for diagnosing early power transformer failures.
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arXiv AI
Jun 24
Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment
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 AI
Aug 3
Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics
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 Machine Learning
Jul 31
Recursive transformers for semiconductor thermo-mechanical reliability
arXiv:2607. 27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design.
arXiv Machine Learning
6d ago
Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations
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 Machine Learning
Jul 20
A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
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.
arXiv AI
Jul 3
Traceable Fault Diagnosis for Battery Energy Storage Systems via Retrieval-Augmented Multi-Agent O&M Assistant
arXiv:2607. 01992v1 Announce Type: new Abstract: Large-scale battery energy storage systems (BESSs) require O&M decisions that combine alarms, cell-level measurements, device topology, diagnostic tables, historical cases, and maintenance documents.
arXiv AI
Jul 28
Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs
arXiv:2607. 22786v1 Announce Type: cross Abstract: In this work, we explore how the inference time of a Transformer Neural Network can be efficiently optimized with applications to real-time anomaly detection in financial time series.
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.
arXiv AI
Jul 28
Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents
arXiv:2607. 22563v1 Announce Type: new Abstract: Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards.
arXiv Machine Learning
Jul 16
Clustering algorithms for multivariate wind farm SCADA data filtering
arXiv:2607. 13544v1 Announce Type: new Abstract: During wind farm operation, Supervisory Control and Data Acquisition (SCADA) systems record numerous anomalies, transients, and specific operational modes, leading to large datasets.
arXiv AI
Jun 19
Controlled Comparison of Machine Learning Models for Fault Classification and Localization in Power System Protection
arXiv:2510. 00831v2 Announce Type: replace Abstract: The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks.