High-voltage cable aluminum sheath corrosion damage identification method, system and equipment based on semi-supervised learning, and medium

• Main Authors: RUAN YAOXUAN, LIU ZHIYONG, LI MENG, SHI YINXIA, HUANG JIASHENG, ZHANG FEI, RAN QIAN, HAN ZHUOZHAN
• Format: Patent
• Language: Chinese; English
• Published: 03.02.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

The invention discloses a high-voltage cable aluminum sheath corrosion damage identification method, system and device based on semi-supervised learning and a medium, and the method comprises the steps: collecting aluminum sheath guided wave monitoring signals without corrosion damage as a source domain data set, and collecting guided wave signals under different corrosion damage conditions as a target domain data set; preprocessing the source domain data set and the target domain data set through waveform alignment and band-pass filtering to obtain a training sample; feature extraction of training samples of source domain and target domain signals is realized through a multi-layer deep convolutional network; a deep feature distance is reduced by training a feature adaptation network, and inherent damage features under different corrosion conditions are obtained; and inputting the inherent characteristics into a structural state classifier, and identifying the corrosion damage in the aluminum sheath. The new