An improved moving window kernel principal component analysis algorithm

SUN Jianping; LI Xue; LIU Xuan; LIU Chen

doi:10.19666/j.rlfd.201704011

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An improved moving window kernel principal component analysis algorithm

更新时间：2025-09-28

- An improved moving window kernel principal component analysis algorithm
- Thermal Power Generation Vol. 47, Issue 1, Pages: 100-105(2018)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院
  2. 中国移动邯郸分公司
  3. 中核新能核工业工程有限责任公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201704011
  CLC： TP301.6
- Published：2018
- 稿件说明：
移动端阅览
SUN Jianping, LI Xue, LIU Xuan, et al. An improved moving window kernel principal component analysis algorithm[J]. Thermal power generation, 2018, 47(1): 100-105.
DOI：

SUN Jianping, LI Xue, LIU Xuan, et al. An improved moving window kernel principal component analysis algorithm[J]. Thermal power generation, 2018, 47(1): 100-105. DOI： 10.19666/j.rlfd.201704011.

摘要

传统滑动窗口主元分析算法在处理工程参数缓慢劣化问题时存在过适应现象

且容易产生误报、漏报的问题。为此

本文以滑动窗口核主元分析(KPCA)算法为基础提出一种基于数据块的改进滑动窗口核主元分析算法。该算法采用MATLAB软件进行数据标准化预处理

利用迭代法剔除采样数据异常值;在检测过程中以数据块为单位进行模型更新

通过调节数据块的大小

使故障诊断更加快速、准确。将该算法和传统核主元算法分别应用于风力发电机组实验数据分析

验证了本文所提出的改进算法能够更好地解决参数缓慢劣化问题。

Abstract

The "over fitting" and "false and leakage alarm" problems easily occur during the application of conventional moving window principal component analysis(PCA) in dealing with slow deterioration of engineering parameters. To solve this problem

this paper proposes an improved moving window kernel principal component analysis(KPCA) algorithm based on data block

according to the sliding window KPCA algorithm. This improved algorithm carries out data standardization by Matlab and applies the iterative method to eliminate the abnormal data of the samples

then it uses the data block as the unit to update the model during the detection. Through regulating the size of the data block

the fault diagnosis will be more rapid and accurate. Moreover

this improved algorithm and the conventional KPCA method were applied to analyze the test data for wind turbine unit

the result verifies the above improved algorithm can solve the problem of slow deterioration of parameters in a better way.

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