基于多源同步感知的发电机散热器故障分析与监测  

Fault Analysis and Monitoring of Generator Radiator Based on Multi-Source Synchronous Perception

作　　者：唐胜菊 潘章 TANG Shengju;PAN Zhang(Sichuan Open University,Chengdu Sichuan 610073,China;Zhenhua Oil Holdings Co.,Ltd.,Beijing 100031,China)

机构地区：[1]四川开放大学,四川成都610073 [2]振华石油控股有限公司,北京100031

出　　处：《北京工业职业技术学院学报》2025年第2期10-15,共6页Journal of Beijing Polytechnic College

基　　金：四川省人社厅2023年四川省高技能人才培养研究课题(CRSZP202310);中国成人教育协会“十四五”成人继续教育科研规划2023年度重点课题(2023-452ZA)。

摘　　要：针对某油田柴油发电机组散热器故障频发问题,提出了一种基于多源信号同步感知的智能监测方案,即通过分层架构设计,集成振动、温度、流量等多源传感器数据,利用深度学习算法实现故障特征的自适应提取与早期预警。与传统的人工监测方法相比,该方案在油田场景下的故障识别准确率提升至95.7%,误报率降低62.3%,年均维修成本下降30.4%,不仅解决了发电机组散热器状态监测的实时性难题,更为野外作业场景下的电力设备智能运维提供了可扩展的技术范式。Aiming at the frequent occurrence of radiator faults in diesel generator set in an oil field,an intelligent monitoring scheme based on multi-source signal synchronous perception is proposed.The scheme integrates multi-source sensor data such as vibration,temperature,and flow through a layered architecture design,and uses deep learning algorithms to achieve adaptive feature extraction and early warning of faults.Compared with traditional manual monitoring methods,this scheme has improved the accuracy of fault recognition in oilfield scenarios to 95.7%,reduced false alarm rates by 62.3%,and reduced annual maintenance costs by 30.4%.It not only solves the real-time monitoring problem of generator set radiator status,but also provides a scalable technical paradigm for intelligent operation and maintenance of power equipment in field operation scenarios.

关 键 词：发电机散热器 故障分析 多源信号 智能监测 

分 类 号：TM314[电气工程—电机]