Updated on 16 Nov 2019
- Used OOP to rewrite some modules.
- Added support for multiple kernel functions.
- Fixed some bugs.
- Accelerated the running speed of the fault diagnosis module
Notice
- For a brief introduction to this code, please read 'contents.m' file.
- Fault diagnosis module only supports gaussian kernel function at this version.
- The fault diagnosis module calls the precompiled file '.mexw64' to accelerate the running speed.
- Class is defined using 'Classdef...End', so this code can only be applied to MATLAB after the R2008a release.
clc
clear all
close all
addpath(genpath(pwd))
% load the training data
load('.\data\helix.mat')
traindata = helix;
% create a parameter structure for the KPCA application
application = struct('type','dimensionalityreduction',...
'dimensionality', 2);
% create an object for kernel function
kernel = Kernel('type', 'gauss', 'width', 24);
% create an object for kpca model
kpca = KpcaModel('application', application,...
'kernel', kernel);
% train KPCA model
model = kpca.train(traindata);
% get the mapping data
mappingdata = model.mappingdata;
% plot the mapping data (only support for 2D or 3D data)
plotMappingData(traindata, mappingdata);
clc
clear all
close all
addpath(genpath(pwd))
% load the training data
load('.\data\circledata.mat')
traindata = circledata;
% create a parameter structure for the KPCA application
application = struct('type','dimensionalityreduction',...
'dimensionality', 2);
% create an object for kernel function
kernel = Kernel('type', 'gauss', 'width', 6);
% create an object for kpca model
kpca = KpcaModel('application', application,...
'kernel', kernel);
% train KPCA model
model = kpca.train(traindata);
% get the mapping data
mappingdata = model.mappingdata;
% plot the mapping data (only support for 2D or 3D data)
plotMappingData(traindata, mappingdata, label);
clc
clear all
close all
addpath(genpath(pwd))
% load the original process data of the TE process
load('.\data\teprocess.mat')
% normalization (in general, this step is important for fault detection)
[traindata, testdata] = normalize(traindata, testdata);
% create a parameter structure for the KPCA application
application = struct('type','faultdetection',...
'cumulativepercentage', 0.75,...
'significancelevel', 0.95);
% create an object for kernel function
kernel = Kernel('type', 'gauss', 'width', 800);
% create an object for kpca model
kpca = KpcaModel('application', application,...
'kernel', kernel);
% train kpca model
model = kpca.train(traindata);
% test KPCA model
testresult = kpca.test(model, testdata);
% visualize the testing results
plotTestResult(model.spelimit, testresult.spe, 'SPE');
plotTestResult(model.t2limit, testresult.t2, 'T2');
*** Detection finished ***
Testing samples number: 960
T2 alarm number : 799
SPE alarm number : 859
clc
clear all
close all
addpath(genpath(pwd))
% load the original process data of the TE process
load('.\data\teprocess.mat')
% normalization (in general, this step is important for fault detection)
[traindata, testdata] = normalize(traindata, testdata);
% create a parameter structure for the KPCA application
application = struct('type','faultdetection',...
'cumulativepercentage', 0.75,...
'significancelevel', 0.95,...
'faultdiagnosis','on',...
'diagnosisparameter', 0.7);
% create an object for kernel function
kernel = Kernel('type', 'gauss', 'width', 800);
% create an object for kpca model
kpca = KpcaModel('application', application,...
'kernel', kernel);
% train kpca model
model = kpca.train(traindata);
% test KPCA model
testresult = kpca.test(model, testdata);
% visualize the testing results
plotTestResult(model.spelimit, testresult.spe, 'SPE');
plotTestResult(model.t2limit, testresult.t2, 'T2');
% create a parameter structure for the fault diagnosis
contribution = diagnoseFault(testresult, ...
'startingtime', 301,...
'endingtime', 500,...
'theta', 0.7);
%
% % visualize the results of fault diagnosis
plotContribution(contribution, 'SPE')
plotContribution(contribution, 'T2')
clc
clear all
close all
addpath(genpath(pwd))
% load the original process data of the TE process
load('.\data\teprocess.mat')
% normalization (in general, this step is important for fault detection)
[traindata, testdata] = normalize(traindata, testdata);
% create a parameter structure for the KPCA application
application = struct('type','faultdetection',...
'cumulativepercentage', 0.75,...
'significancelevel', 0.95,...
'timelag', 60);
% create an object for kernel function
kernel = Kernel('type', 'gauss', 'width', 800);
% create an object for kpca model
kpca = KpcaModel('application', application,...
'kernel', kernel);
% train kpca model
model = kpca.train(traindata);
% test KPCA model
testresult = kpca.test(model, testdata);
% visualize the testing results
plotTestResult(model.spelimit, testresult.spe, 'SPE');
plotTestResult(model.t2limit, testresult.t2, 'T2');
*** Detection finished ***
Testing samples number: 960
T2 alarm number : 768
SPE alarm number : 777
