A Husqvarna researcher developed a fast, interpretable PV hotspot-detection method using IR thermography and Lab* color-space features instead of heavy neural networks, achieving up to 95.2% accuracy ...

Fault detection and diagnosis (FDD) in HVAC systems is a critical area of research that seeks to optimise building performance, improve energy efficiency and ensure occupant comfort. Recent approaches ...

AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...

A group of researchers led by the University of Sharjah in the UAE proposed to use the convolutional neural network (CNN) technique to detect temperature and shading-induced faults in PV modules. CNN ...

AWARE uses waveform signatures to detect and classify early-stage grid faults, enabling proactive intervention. The system combines physics-based models with AI/ML to interpret subtle electrical ...

In today's industries, quality inspection in semiconductor manufacturing is critical. Many traditional fault detection and diagnosis techniques have been developed to determine the existence of trends ...

IC manufacturers are increasingly relying on intelligent data processing to prevent downtime, improve yields, and reduce scrap. They are integrating that with fault detection and classification (FDC) ...

PPL Electric Utilities has been recognized with the 2025 Smart Electric Power Alliance (SEPA) Power Player Resilience Award for the use of predictive failure technology to enhance public safety, ...