EN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
- 发电技术 2021年42卷第4期 页码：489-499
- 作者机构：
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
- 作者简介：
[ "陈尚年(1997), 男, 硕士研究生, 研究方向为动力机械状态检测与故障诊断, 920191849@qq.com" ]
李录平(1963), 男, 教授, 博士生导师, 研究方向为动力机械状态检测与故障诊断, 本文通信作者, cs_liluping@163.com
- 基金信息：国家重点研发计划项目(2017YFB0903600);南方电网公司重点科技项目(GZKJXM20172214)
DOI：10.12096/j.2096-4528.pgt.21048
中图分类号： TK05收稿：2021-04-30，
纸质出版：2021-08-31
- 稿件说明：
移动端阅览
陈尚年, 李录平, 张世海, 等. 汽轮发电机组振动故障诊断技术研究进展[J]. 发电技术, 2021,42(4):489-499.
Shangnian CHEN, Luping LI, Shihai ZHANG, et al. Research Progress of Vibration Fault Diagnosis Technology for Steam Turbine Generator Sets[J]. Power Generation Technology, 2021, 42(4): 489-499.
陈尚年, 李录平, 张世海, 等. 汽轮发电机组振动故障诊断技术研究进展[J]. 发电技术, 2021,42(4):489-499. DOI： 10.12096/j.2096-4528.pgt.21048.
Shangnian CHEN, Luping LI, Shihai ZHANG, et al. Research Progress of Vibration Fault Diagnosis Technology for Steam Turbine Generator Sets[J]. Power Generation Technology, 2021, 42(4): 489-499. DOI： 10.12096/j.2096-4528.pgt.21048.
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。
Abstract
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
Keywords
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