电力设备缺陷文本的双通道语义增强网络挖掘方法

Dual-channel Semantic Enhancement Network Mining Method for Defect Text of Power Equipment

电力设备运维环节积累的缺陷文本可指导设备的状态评价和检修工作。然而缺陷文本结构多样且背景噪声强,导致智能挖掘信息的难度大。针对该问题,提出了基于双通道语义增强网络的电力设备缺陷文本挖掘方法。首先,分析缺陷文本的内容,结合自然语言处理方法预处理缺陷文本。利用Glove词向量嵌入模型将缺陷文本映射至数值空间表征语义。然后,基于词移距离构建缺陷文本的增强文本,通过含注意力机制的双向长短时记忆神经网络分别提取缺陷文本和增强文本的特征,进而在网络末端融合特征实现关键信息加强,提升模型分类性能。实例表明,所提双通道语义增强网络的分类Macro-F1指标相比于传统机器学习方法、单通道深度学习方法至少提高6.2%、5.2%,同时所提方法为实现图像、文本等多源运维数据的特征增强提供新思路。

Defect texts accumulated in the operation and maintenance of power equipment may guide the condition evaluation work and overhaul work.However,the complex structure and strong background noise of the defect records lead to the difficulty of information mining intelligently.To address this problem,this paper proposes a dual-channel semantic enhancement network model based on defect text mining.Firstly,the content of the defective text is analyzed,and the defect text is pre-processed by the methods of natural language processing.And the Glove word vector embedding model is used to map the defect text to the numerical space to express the semantics.Then the enhanced text of the defect text is constructed based on word moving distance,and the defect text and enhanced text features are extracted by a bidirectional long-short term memory neural network with an attention mechanism.The key information is enhanced by feature fusion at the end of the network to improve the model effect of classification.The examples show that the classification Macro-F1 metrics of the proposed dual-channel semantic enhancement network are at least 6.2%and 5.2%higher than those of traditional machine learning methods and single-channel deep learning methods,and the proposed method provides a new idea for feature enhancement of multi-source operational data such as images and text.