A Compound KPCA Fault Diagnosis Model

吕宁; 刘少波; 于晓洋

您当前的位置：

首页 >

文章列表页 >

A Compound KPCA Fault Diagnosis Model

更新时间：2026-09-14

- A Compound KPCA Fault Diagnosis Model
- 一种复合KPCA故障诊断模型
- Vol. 30, Issue 6, Pages: 555-560(2009)
- 作者机构：
  
  哈尔滨理工大学自动化学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2009
- 稿件说明：
移动端阅览
吕宁, 刘少波, 于晓洋. A Compound KPCA Fault Diagnosis Model[J]. 2009, 30(6): 555-560.
DOI：

吕宁, 刘少波, 于晓洋. A Compound KPCA Fault Diagnosis Model[J]. 2009, 30(6): 555-560. DOI：

摘要

核函数主元分析(KPCA)故障诊断方法中核函数的具体形式对诊断性能的影响非常大.针对核函数具体形式选取问题

基于径向基高斯核函数和一类特定多项式核函数

构造出一种新的复合核函数模型.对模型的构造方法进行了论述

给出了具体的故障诊断算法的实现步骤.该模型在兼顾全局信息提取的前提下

保证了局部灵敏度

具有很好的拟合能力.通过与其他算法仿真比较表明所提出方法不但可以避免对模型的事先假设

且具有较高的故障诊断效率.

Abstract

The concrete form of kernel function in the KPCA fault diagnosis model has great impact on the diagnosis performance.A new compound kernel function model is presented based on RBF Gaussian kernels and polynomial kernels.The construction of the model and the implementation steps of the fault diagnosis algorithms are given.In the condition of considering the global information the model guarantees partial sensitivity and good fitting ability.Compared with other KPCA algorithms' simulation results

the proposed model not only avoids prior hypothesis but also has higher fault diagnosis efficiency.

关键词

Keywords

references

Pentland A,Moghaddam B,Starner T,et al.View-based and modular eigenspaces for face recognition[C].ProcIEEE Computer Society Conference On Computer Vision and Pattern Recognition,Seattle,1994:84-91.

Dong D,Mcavoy T J.Batch tracking via nonlinear principal component analysis[J].AICHE Jounnal,1996,42(8):2199-2208.

Schlkopf B,Smola A,Muller K.Nonlinear component analysis as a kernel eigenvalue problem[J].NeuralComputation,1998,10(5):1299-1399.

Shaw-Taylor J,Cristianini N.Kernel methods for pattern analysis[M].London:Cambridge University Press,2004.

John S T,Nello C.Kernel methods for pattern analysis[M].England:Cambridge University Press,2004.

Vapnik V,Chapelle O.Bounds on error expectation for support vector machines[J].Neural Computation,2000,12(9):2013-2036.

Scholkpf B.Support Vector Learning[D].Berlin:Berlin University,1997.

Smola A J.Learning with kernels[D].Berlin:Technical University of Berlin,1998.

Baesens B,Viaenc S,Van G T,et al.An empirical assessment of kernel type performance for least squares supportmachine classification[C].Proceedings of Fourth Internaional Conference on Knowledte-Based Intelligent EngineerSystem and Allied Technologyis,Brighton,2000:313-316.

Witten I H,Frank E.数据挖掘:实用机器学习技术[M].董琳,邱泉,于晓峰,等,译.北京:机械工业出版社,2006.

Lee J M,Yoo C K,Lee I B.Fault detection of batch processes using multiwav kernel principal component analysis[J].Computers and Chemical Engineering,2004,28(9):1837-1840.

Nomikos P,Macgregor J F.Monitoring batch processes using multiway principal component analysis[J].AICHEJournal,1994,40(8):1361-1375.

Views

235

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Fault Diagnosis Method for RV Reducer Under Unknown Rotating Speed

Fault Diagnosis of Switch Machine Based on Visibility Graph Feature and CatBoost

Feature Parameter Structure of Motor Bearing Fault Based on Wavelet and Cosine Transform

基于EEMD结合二次小波包降噪的齿轮箱故障诊断

基于遗传优化BP神经网络的供热管网故障诊断模型

Related Author

TANG Jing

LIN Changpeng

LIU Jing

LAN Jiang

WEI Kaixiang

WANG Zhichao

HUANG Jinying

JING Licheng

Related Institution

AECC Guizhou Liyang Aviation Power Co.， Ltd.

Data Science and Technology， North University of China

School of Mechanical Engineering， North University of China

School of Information and Communication Engineering， Shanxi Institute of Electronic Science and Technology

太原工业学院自动化系

⁰