Study on abnormal diagnosis of sensor in monitoring system based on KPCA

PENG Hong-xing; PAN Li-li; ZHAO Hong-tu

doi:10.13873/J.1000-9787(2016)12-0033-04

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Study on abnormal diagnosis of sensor in monitoring system based on KPCA

更新时间：2025-09-11

- Study on abnormal diagnosis of sensor in monitoring system based on KPCA
- Vol. 35, Issue 12, Pages: 33-36(2016)
- 作者机构：
  
  1. 河南理工大学计算机科学与技术学院
  2. 煤炭安全生产河南省协同创新中心
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)12-0033-04
  CLC： TD712.6;TP277
- Published：2016
- 稿件说明：
移动端阅览
[1]彭红星,潘梨莉,赵鸿图.基于KPCA的监控系统传感器异常诊断方法[J].传感器与微系统,2016,35(12):33-36.

PENG Hong-xing, PAN Li-li, ZHAO Hong-tu. Study on abnormal diagnosis of sensor in monitoring system based on KPCA[J]. 2016, 35(12): 33-36.
[1]彭红星,潘梨莉,赵鸿图.基于KPCA的监控系统传感器异常诊断方法[J].传感器与微系统,2016,35(12):33-36. DOI： 10.13873/J.1000-9787(2016)12-0033-04.

PENG Hong-xing, PAN Li-li, ZHAO Hong-tu. Study on abnormal diagnosis of sensor in monitoring system based on KPCA[J]. 2016, 35(12): 33-36. DOI： 10.13873/J.1000-9787(2016)12-0033-04.

摘要

针对监控系统数据异常时

故障检测准确性不高的问题

提出一种基于监控系统传感器异常的核主元分析（KPCA）检测方法。利用平方预报误差（SPE）统计量和均方贡献值法进行故障检测和故障源的定位

改善了主元分析（PCA）应用于非线性系统故障检测准确性低的问题。分别利用基于KPCA和PCA的故障检测模型进行仿真比较。实验结果表明:KPCA提高了非线性监控系统传感器异常诊断的准确性。

Abstract

Aiming at problem of low accuracy of fault detection when abnormal data occurs in monitoring system

put forward a detecting method of kernel principal component analysis（ KPCA）

which is based on sensor abnormal of monitoring system. The method KPCA detects faults and locates the fault source via the statistics of square prediction error（ SPE） and the mean square contribution value from KPCA method

improve problem of low accuracy of PCA applied to fault detecting of nonlinear system. Fault detecting models based on KPCA and PCA are used for simulation and comparison. Experimental results show that KPCA improve accuracy of sensor abnormal diagnosis of nonlinear monitoring system.

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references

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多传感器信息融合在滚动轴承故障诊断中的应用 [J]. 马文龙,吕建新,吴虎胜,黄炯龙. 传感器与微系统 . 2013(07)

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Nonlinear process monitoring using kernel principal component analysis [J] . Jong-Min Lee,ChangKyoo Yoo,Sang Wook Choi,Peter A. Vanrolleghem,In-Beum Lee. Chemical Engineering Science . 2003 (1)

Nonlinear Component Analysis as a Kernel Eigenvalue Problem [J] . Bernhard Schö,lkopf,Alexander Smola,Klaus-Robert Mü,ller. Neural Computation . 1998 (5)

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