汽轮发电机组振动故障诊断技术研究进展  

Research Progress of Vibration Fault Diagnosis Technology for Steam Turbine Generator Sets

作　　者：陈尚年 李录平[1] 张世海 欧阳敏南 樊昂 文贤馗 CHEN Shangnian;LI Luping;ZHANG Shihai;OUYANG Minnan;FAN Ang;WEN Xiankui(School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410014,Hunan Province,China;Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,Guizhou Province,China)

机构地区：[1]长沙理工大学能源与动力工程学院,湖南省长沙市410014 [2]贵州电网有限责任公司电力科学研究院,贵州省贵阳市550002

出　　处：《发电技术》2021年第4期489-499,共11页Power Generation Technology

基　　金：国家重点研发计划项目(2017YFB0903600);南方电网公司重点科技项目(GZKJXM20172214)。

摘　　要：高参数大容量汽轮发电机组的安全稳定运行对电力生产具有重要意义。综述了汽轮发电机组振动故障的机理、信号检测、信号分析、特征提取以及故障诊断方法。针对传统的智能诊断方法面临采样数据量大、信号特征提取困难、故障训练样本不足等问题,介绍了先进的传感技术和以深度学习为代表的新一代智能机器学习技术。通过分析得出结论:未来汽轮发电机组振动故障诊断技术应以人工智能、大数据、云计算等技术为核心,融合虚拟化及三维可视化技术,实现故障诊断的速度与精度相统一。With the increasing demand of power energy,the safe and stable operation of high parameter and large capacity turbo-generator sets is of great significance to power production.The vibration fault mechanism,signal detection,signal analysis,feature extraction and fault diagnosis methods of turbine generator set were summarized,respectively.Moreover,an advanced sensing technology and a new generation of intelligent machine learning technology represented by deep learning were introduced to solve the problems that traditional intelligent diagnosis methods are faced with,such as large amount of sampled data,difficulty in extracting signal features and shortage of fault training samples.It is summarized that the future vibration fault diagnosis technology of turbo generator sets should be based on artificial intelligence,big data,and cloud computing,supplemented by fusion virtualization and three-dimensional visualization technology,to achieve the unity of fault diagnosis speed and accuracy.

关 键 词：汽轮发电机组 特征提取 故障诊断 人工智能 大数据 云计算 深度学习 

分 类 号：TK05[动力工程及工程热物理]