There are many kinds of defects in power equipment, some of which will lead to equipment fault. Defects detection timely of in power equipment is an essential means to prevent equipment. It aims to identify trigger words from the text and divide the text into corresponding device 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, device defect detection methods based on deep learning are studied. We design a pre-training language model for device defect detection in the power field, utilizing event triplet knowledge. In addition, we construct a power equipment defect detection data set. The representation ability of the language model is improved by event triplet pre-training before the defect detection task. Experimental results show that the pre-trained model performs better in the event detection task and can effectively realize the defect detection.
