王宏刚,纪鑫,武同心,杨智伟,何禹德.基于预训练语言模型的电力领域设备缺陷检测[J].电测与仪表,2022,59(5):180-186. Wang Honggang,Ji Xin,Wu Tongxin,Yang Zhiwei,He Yude.Device defect detection in power field based on pre-trained language model[J].Electrical Measurement & Instrumentation,2022,59(5):180-186.
基于预训练语言模型的电力领域设备缺陷检测
Device defect detection in power field based on pre-trained language model
There are many kinds of defects in electrical equipment, which may lead to electrical equipment faults. Timely detection of defects is an essential means to prevent equipment failure. It aims to identify triggers from the text and divide the text into corresponding equipment defect types. Aiming at the problems of insufficient annotation of defect data set and difficulty in semantic understanding due to numerous professional terms in the text, we study the device defect detection method based on deep learning, and design a pre-trained language model for device defect detection in the power field by utilizing event triplet knowledge. In addition, we construct an electrical equipment defect detection data set. The representation ability of the language model is improved by event triplet pre-trained before the defect detection task of the model. Experimental results show that the pre-trained model based on field equipment case data performs better in the event detection task and can effectively realize the defect detection of power field defect report text.
