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

Shangnian CHEN; Luping LI; Shihai ZHANG; Minnan OUYANG; Ang FAN; Xiankui WEN

doi:10.12096/j.2096-4528.pgt.21048

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

Intelligent Turbine Power Generation Technology | 更新时间：2025-07-04

- Research Progress of Vibration Fault Diagnosis Technology for Steam Turbine Generator Sets
- Power Generation Technology Vol. 42, Issue 4, Pages: 489-499(2021)
- 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：
- 基金信息：
  
  National Key Research and Development Program of China(2017YFB0903600);Key Science and Technology Project of China Southern Power Grid Corporation(GZKJXM20172214)
- DOI：10.12096/j.2096-4528.pgt.21048
  CLC： TK05
- Received：30 April 2021，
  
  Published：31 August 2021
- 稿件说明：
移动端阅览
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：

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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