Research on Fault Condenser Feature Extraction for Multi-modal Processes

ZHU Jitao; ZENG Shuiping; HE Yuqing; ZHENG Jiajia; DAI Yuchen; SI Fengqi

doi:10.19805/j.cnki.jcspe.2025.230679

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Research on Fault Condenser Feature Extraction for Multi-modal Processes

更新时间：2026-02-05

- Research on Fault Condenser Feature Extraction for Multi-modal Processes
- Chinese Journal of Power Engineering Vol. 45, Issue 1, Pages: 124-130(2025)
- 作者机构：
  
  1. 国家电投集团江西电力有限公司,江西,南昌,330000
  2. 国家电投集团江西电力有限公司景德镇发电厂,江西,景德镇,333036
  3. 东南大学能源与环境学院,江苏,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.230679
  CLC： TM621
- Online First：15 January 2025，
  
  Published：2025
- 稿件说明：
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朱继涛,曾水平,贺宇清,郑佳佳,代雨辰,司风琪. 面向多模态过程的凝汽器故障特征提取研究动力工程学报, 2025, 45(1): 124-130 https://doi.

org/10.19805/j.cnki.jcspe.2025.230679
朱继涛,曾水平,贺宇清,郑佳佳,代雨辰,司风琪. 面向多模态过程的凝汽器故障特征提取研究动力工程学报, 2025, 45(1): 124-130 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230679.

org/10.19805/j.cnki.jcspe.2025.230679 DOI：

摘要

针对凝汽器设备多模态运行过程的在线监测与故障特征提取问题

提出了一种融合

-means聚类与重构主成分分析的故障特征提取方法。首先

采用

-means聚类进行模态识别

聚类结果表明不同模态之间平方预测误差(SPE)统计量存在显著差异

需要对子模态分别建立监测模型。为有效获取故障特征

抑制故障分离过程中的残差污染现象

采用重构主成分分析法获取故障特征向量

实现了面向多模态过程的故障特征提取。结果表明:使用重构贡献图法能够分离检测出故障变量及其特征向量

且能有效避免残差污染问题

具有良好的故障定位精度。

Abstract

To address the issue of online monitoring and fault feature extraction in multi-modal operation process of condenser equipment

a fault feature extraction method integrating

-means clustering and reconstructed principal component analysis was proposed. First

-means clustering was used to identify modes. The clustering results show significant differences in squared prediction error (SPE) statistics among different modes

and it is necessary to establish monitoring models for sub-modes. In order to obtain fault features effectively and suppress residual pollution in fault separation process

fault feature vectors were obtained by reconstructing principal component analysis method

and the fault feature extraction for multi-modal

process was realized. Results show that fault variables and their feature vectors can be separated and detected by using the reconstructed contribution graph method

and the residual pollution problem can be avoided effectively

and the fault location accuracy is good.

关键词

Keywords

references

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