Fault diagnosis model for steam turbine vibration based on feature reduction theory

WANG Pan; WANG Yongtao; WANG Baoyu

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Fault diagnosis model for steam turbine vibration based on feature reduction theory

更新时间：2025-09-28

- Fault diagnosis model for steam turbine vibration based on feature reduction theory
- Thermal Power Generation Vol. 45, Issue 2, Pages: 96-100(2016)
- 作者机构：
  
  1. 华北电力大学能源动力与机械工程学院
  2. 华电国际电力股份有限公司
  3. 华电电力科学研究院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TK268.1
- Published：2016
- 稿件说明：
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WANG Pan, WANG Yongtao, WANG Baoyu. Fault diagnosis model for steam turbine vibration based on feature reduction theory[J]. Thermal power generation, 2016, 45(2): 96-100.
DOI：

WANG Pan, WANG Yongtao, WANG Baoyu. Fault diagnosis model for steam turbine vibration based on feature reduction theory[J]. Thermal power generation, 2016, 45(2): 96-100. DOI：

摘要

通过比较主成分分析和粗糙集理论两种特征约简方法

提出了将主成分分析与改进支持向量机相结合的故障诊断方法

建立了主成分分析集成特征约简的振动故障诊断模型。以某汽轮机油膜涡动振动故障信号及某600MW机组汽轮机的质量不平衡振动为例

对本文故障诊断方法进行验证

结果表明

本文故障诊断方法既缩短了诊断时间

又具有较高的诊断准确率。

Abstract

By comparing two types of feature reduction methods

the principal component analysis and the rough set theory

the integrated feature reduction of vibration fault diagnosis model of principal component analysis was established

applying the improved support vector machine platform.Taking the fault signal of oil film whirl vibration in a steam turbine and the mass unbalance vibration in a 600 MW unit steam turbine as the example

the new fault diagnosis method combing the principal component analysis with the improved support vector machine was verified.The results show that

this new fault diagnosis method not only can shorten the diagnosis time

but also has a high diagnostic accuracy

it is effective and feasible.

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