[1]陈 宏,陈新财,巩晓赟,等.基于知识图谱的风电机诊断系统构建与应用[J].郑州大学学报(工学版),2023,44(06):54-60.[doi:10.13705/j.issn.1671-6833.2023.06.007]
　CHEN Hong,CHEN Xincai,GONG Xiaobin,et al.Construction and Application of Wind Turbine Diagnosis System Based on Knowledge Graph[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):54-60.[doi:10.13705/j.issn.1671-6833.2023.06.007]
点击复制

基于知识图谱的风电机诊断系统构建与应用()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44卷
期数:
2023年06期
页码:
54-60
栏目:
出版日期:
2023-09-25

文章信息/Info

Title:
Construction and Application of Wind Turbine Diagnosis System Based on Knowledge Graph
作者:
陈 宏;  陈新财;  巩晓赟;  韩东洋;  刘华杰
1. 郑州大学 机械与动力工程学院,河南 郑州 450001;2. 哈密职业技术学院 机电系,新疆 哈密 839099;3. 郑州轻 工业大学 机电工程学院,河南 郑州 450000
Author(s):
CHEN Hong;  CHEN Xincai;  GONG Xiaobin;  HAN Dongyang;  LIU Huajie
1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Department of Mechanical and Electrical, Hami Vocational and Technical College, Hami 839099, China; 3. School of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
关键词:
知识图谱;  知识抽取;  风力发电机;  故障诊断;  本体
Keywords:
knowledge graph;  knowledge extraction;  wind turbine;  fault diagnosis;  ontology
DOI:
10.13705/j.issn.1671-6833.2023.06.007
文献标志码:
A
摘要:
针对风力发电机故障诊断与维修过程不明确以及历史故障数据记录大量遗留等问题,提出一种以知识图 谱的方式构建的风力发电机故障诊断系统。 首先,通过改进的命名实体识别模型 BERT-BiLSTM-CRF 对故障文本 进行知识抽取。 数据集采用了近 10 年来的风力发电机故障案例、事故分析等文本数据。 实验结果表明:在风力发 电机故障领域中,改进的实体识别方法相比于传统模型效果提升了 2. 54%。 其次,对抽取的知识实体进行结构化 分析,由于传统故障树在实际故障推理中缺乏目的性,且每个底事件相对于顶事件的重要性不同,提出以故障的特 征属性为分支条件引入到故障树推理中,完成故障树定性与定量分析,并结合故障模式影响和危害性分析( FMECA)完善故障领域知识模型;再对知识结构完成本体化建模,使用 Prot􀆧g􀆧 开发工具对故障树结构完成了基于六元 组概念的本体建模,使构建的本体知识库满足推理的前提条件。 最后,通过 Neo4j 实现知识模型的可视化,并提升 了知识数据的读写能力。
Abstract:
To address the precision problems in wind turbine fault diagnosis and maintenance processes, the lack of management of fault domain knowledge, and the large amount of historical fault data records left behind, a wind turbine fault diagnosis system was proposed to be constructed in the form of a knowledge graph. Firstly, knowledge extraction of fault texts was carried out by an improved named entity recognition model BERT-BiLSTM-CRF. The data set used text data of wind turbine fault cases and accident analysis in the past 10 years. And it was proved through experiments that the improved entity recognition method was 2. 54% more effective compared to the traditional model in the wind turbine fault domain. The extracted knowledge entities were then structurally analysed. As the traditional fault tree lacked purpose in actual fault reasoning, and each bottom event had different levels of importance to the top event, it was proposed that the characteristic attributes of the fault was introduced, as branching conditions, into the fault tree reasoning, to complete the fault tree qualitative and quantitative analysis, and the fault mode impact and hazard analysis ( FMECA) were combined to refine the fault domain knowledge model. Then Protg development tools were use to complete the ontology modelling of the fault tree structure based on the six-tuple concept, so that the constructed ontology knowledge base could meet the prerequisites for inference. Finally, the visualization of knowledge model was realized by Neo4j, and the ability of reading and writing knowledge data was improved.
更新日期/Last Update: 2023-10-22