主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2025, Vol. 44 ›› Issue (7): 897-902.

• • 上一篇    下一篇

基于综合集成法的压缩空气泡沫灭火系统故障诊断及应用

黄玉彪1,2,3, 冯旭4,5,6, 张佳庆1,2,3, 过羿1,2,3   

  1. （1.国网安徽省电力有限公司电力科学研究院，安徽 合肥 230601； 2.电力火灾与安全防护安徽省重点实验室，安徽 合肥 230601； 3.国家电网公司输变电设施火灾防护实验室，安徽 合肥 230601； 4.应急管理部天津消防研究所，天津 300381； 5.工业与公共建筑火灾防控技术应急管理部重点实验室，天津 300381； 6.天津市消防安全技术重点实验室，天津 300381）
  • 收稿日期:2024-07-08 修回日期:2024-09-13 出版日期:2025-07-24 发布日期:2025-07-15
  • 作者简介:黄玉彪，国网安徽省电力有限公司电力科学研究院高级工程师，博士，主要从事安全科学与工程研究，安徽省合肥市经济技术开发区紫云路299号，230601，firelab_huang@16.com。
  • 基金资助:
    国家电网公司总部科技项目（5700?202320280A?1?1?ZN）

Fault diagnosis and application of compressed air foam fire fighting system based on the meta-synthetic approach

Huang Yubiao1,2,3, Feng Xu4,5,6, Zhang Jiaqing1,2,3, Guo Yi1,2,3   

  1. (1. State Grid Anhui Electric Power Research Institute, Hefei Anhui 230601, China; 2. Anhui Province Key Laboratory of Electric Fire and Safety Protection, Hefei Anhui 230601, China; 3. State Grid Laboratory of Fire Protection for Transmission and Distribution Facilities, Hefei Anhui 230601, China; 4. Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300381, China; 5. Key Laboratory of Fire Protection Technology for Industry and Building, Ministry of Emergency Management, Tianjin 300381, China; 6. Tianjin Key Laboratory of Fire Safety Technology, Tianjin 300381, China)
  • Received:2024-07-08 Revised:2024-09-13 Online:2025-07-24 Published:2025-07-15

摘要： 压缩空气泡沫灭火系统（CAFS）在特高压换流站等关键设施中发挥着重要作用。然而，随着其灭火能力的提升，管控难度也加大，对消防设施的健康状态监测、故障诊断及可靠性分析提出了更高要求。针对CAFS功能结构复杂、故障模式多样的特点，本文融合时序监测数据、知识图谱和灰色关联分析3种典型故障诊断方法，构建了故障知识图谱模型，通过指定故障关键词查询知识图谱，结合灰色关联分析算法，能够精准锁定故障点位，为维保和管理人员提供决策支持。开展了3种典型故障模拟试验，试验结果表明，本文提出的综合集成方法能够有效提高故障诊断的效率和实时性，对保障CAFS系统的可靠性和有效性具有重要意义。

关键词: 压缩空气泡沫灭火系统, 综合集成法, 故障知识图谱, 灰色关联分析

Abstract: CAFS plays an important role in critical facilities such as UHV converter stations. However, with the improvement of its fire extinguishing capability, the difficulty of management and control has also increased, posing higher requirements for health status monitoring, fault diagnosis, and reliability analysis of fire protection facilities. Given the complexity of the functional structure and diverse fault modes of CAFS, this paper integrates three typical fault diagnosis methods, including time-series monitoring data, knowledge graph, and grey relational analysis, to construct a fault knowledge graph model. By querying the knowledge graph with specified fault keywords and combining with the grey relational analysis algorithm, the fault points can be precisely located, providing decision support for maintenance and management personnel. Three typical fault simulation experiments were conducted, and the experimental results show that the proposed meta-synthetic method can effectively improve the efficiency and accuracy of fault diagnosis, which is of great significance for ensuring the reliability and effectiveness of the CAFS system.

Key words: compressed air foam system, meta-synthetic approach, fault knowledge graph, grey relational analysis