pv magazine International

Ensemble deep learning for PV cell defect detection

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV ...
Nature

Magnetic Flux Leakage Testing and Defect Detection

Magnetic flux leakage (MFL) testing is a widely established non‐destructive evaluation technique used to assess the integrity of ferromagnetic materials in applications such as pipeline inspection and ...
Science Daily

Researchers use machine learning to detect defects in additive manufacturing

Researchers have developed a new method for detecting defects in additively manufactured components. Researchers at the University of Illinois Urbana-Champaign have developed a new method for ...
pv magazine International

US researchers use machine learning to detect hidden solar array defects

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...
EurekAlert!

Researchers use machine learning to detect defects in additive manufacturing

Longitudinal (top) and axial (middle) images of X-Ray CT data of parts with 6 internal defects: a spherical clog, a stellated shaped clog, a cone shaped void, a blob shaped void, an elliptical warp of ...