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Kernel Principal Component Analysis (KPCA)

version 2.1.1 (1.64 MB) by Kepeng Qiu
MATLAB Code for non-linear dimensionality reduction, fault detection, and fault diagnosis through the use of kernels.

31 Downloads

Updated 18 May 2020

GitHub view license on GitHub

MATLAB Code for non-linear dimensionality reduction, fault detection, and fault diagnosis through the use of kernels.

** if this code is helpful for you, please rate stars for this code.

** Main features
1. Easy-used API for training and testing KPCA model
2. Multiple kinds of kernel functions
3. Support for dimensionality reduction, fault detection, and fault diagnosis
4. Support for data reconstruction

** Notices
1. Only fault diagnosis of Gaussian kernel is supported.
2. Class is defined using 'Classdef...End', so this code can only be applied to MATLAB after the R2008a release.
3. More details and discussions please see: https://www.ilovematlab.cn/thread-560380-1-1.html
4. This code is for reference only.
5. Please go to the GitHub to submit a discussion, because here I cannot reply to you because of the limitations of the web page. https://github.com/iqiukp/Kernel-Principal-Component-Analysis-KPCA/issues

Comments and Ratings (6)

zhao botao

Md. Tanjin Amin

Hi Kepeng Qiu,

Thanks for such nice work. I was just wondering do you have any code on fault detection and diagnosis using kernel ICA? If you have, can you please share it?

Xy Zou

enjian cai

Dinie Muhammad

Shanfei Su

Thanks for your great algorithm of KPCA(with Fault Diagnosis). It's owesome to solve the nonlinear problem.
When I rethink the algorithm, I think of that the algorithm belongs to Static KPCA,what if develop a dynamic KPCA method? It seems that there will be a more accurate diagnosis. So I try it.
But I failed to finish it (maybe because I do a wrong thing while making the augmented matrix ).
Sincerely, can you make a further step to realize the algorithm of dynamic KPCA?
Thanks again for your great work!

Here are some references:
1. Mingxing Jia ⁎, Fei Chu.On-line batch process monitoring using batch dynamic kernel principal component analysis(J).
2. Ines Jaffel a, OkbaTaouali. Moving window KPCA with reduced complexity for nonlinear dynamic process monitorin(J).
3. YUAN Zhe,SHI Huaitao. Fault Diagnosis Approach Based on Step Dynamic KPCA(J)

P.S. I also followed you at Github.

Updates

2.1.1

1. Added some descriptions

2.1

1. added support for data reconstruction

2.0

1. Used OOP to rewrite some modules.
2. Added support for multiple kernel functions.
3. Fixed some bugs.
4. Accelerated the running speed of the fault diagnosis module.

1.2

1. Fixed some errors
2. Added Dynamic KPCA(DKPCA)

1.1.1

1. Fixed some errors
2. Added fault diagnosis

1.1

1. Fixed some errors
2. Added fault diagnosis

MATLAB Release Compatibility
Created with R2020a
Compatible with R2008a to any release
Platform Compatibility
Windows macOS Linux