基于知识图谱的水电站设备故障根因分析方法
Root cause analysis method for equipment failure of hydropower stations based on knowledge graph
谈群 1苗洪雷 2秦拯 3朱玺 2郜振亚2
作者信息
- 1. 湖南大学信息科学与工程学院,湖南长沙 410082;华自科技股份有限公司,湖南长沙 410000
- 2. 华自科技股份有限公司,湖南长沙 410000
- 3. 湖南大学信息科学与工程学院,湖南长沙 410082
- 折叠
摘要
水电站设备故障成因复杂、关联性强,研究故障之间的成因关系及发生概率有助于快速确定故障原因和制定排查计划.根据专家经验与历史故障数据构建了水电站设备知识图谱,设计了基于知识图谱的智能故障诊断算法,利用Noisy Or模型实现一种近似推理算法,实现了根因的定量分析,并基于图推理分析相关现象和熵理论实现了排查建议的优化计算.该系统可给出全面、详细的建议和解释信息,允许用户自由交互,可以帮助用户快速开展排查故障.系统具有不依赖历史数据、准确性高、可解释性强、可动态更新等优点,为智慧水电站建设提供了先进平台.
Abstract
The causes of equipment failures of hydropower stations are complex and highly correlated,so the study on the se-quence relationship and probability of failure is helpful to quickly determining the cause of failure and making a troubleshooting plan.According to the expert experience and historical fault data,a knowledge graph of hydropower equipment was constructed,and an intelligent fault diagnosis algorithm based on the knowledge graph was designed.An approximate reasoning algorithm was implemented by using the Noisy Or model to realize the quantitative analysis of root causes,and the optimization calculation of troubleshooting suggestions was realized based on the graph reasoning analysis of related phenomena and entropy theory.The sys-tem provides comprehensive and detailed suggestions and explanation information,allows users to interact freely,and helps users quickly find faults.The system has the advantages of being independent on historical data,high accuracy,strong interpretability and dynamic updating,which provide an advanced platform for construction of intelligent hydropower stations.
关键词
水电站设备/故障诊断/知识图谱/图推理/熵Key words
equipment of hydropower station/fault diagnosis/knowledge graph/graph reasoning/entropy引用本文复制引用
基金项目
国家自然科学基金(U20A20174)
长沙市科技计划项目(kh2204007)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据