University Laval Infrared Thermography Databases for Deep Learning Multiple Types of Defect Detections Training

Nowadays, automatic defect detection research by deep learning algorithms plays a crucial role, especially for non-destructive evaluation with infrared thermography. In deep learning research, the databases are the Achilles’ heel during the training in order to preserve optimized performance. In this work, we will present the infrared thermography sequences databases from the Universite Laval Multipolar Infrared Vision Infrarouge Multipolaire (MIVIM) research group for regular and irregular defect analysis in order to provide the best data collection resources for the pretraining of convolutional neural network and feature extraction analysis with future researchers and engineers. The databases will include infrared thermography sequences from regular and irregular defects of carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), plexiglass, aluminum, and steel, which could be available online for public use and research purposes.

Electronic Current Transformer Defects Statistical Analysis of Intelligent Substation

In order to master operation status of the electronic current transformer(ECT), application investigation and defects statistical analysis were conducted on ECT of 110(66)kV~1000kV applied in smart substation of the State Grid Corporation of China(SGCC). The defect location, defect cause and defect type of ECT with different principles are analyzed. The results indicate that the defects of ECT are mainly concentrated in the acquisition unit, the defect rate is closely related to the reliability of electronic components, the technical level, production process and quality control of the manufacturer. In view of the typical defect analysis, the corresponding solution measures are put forward to provide reference for the subsequent research and application of ECT.