PCA-based Fault Detection for 2D Multivariate Process Data

versión 1.0.0.0 (16.5 KB) por Karl Ezra Pilario
Fault detection in a simple process using PCA and Kernel Density Estimation

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Actualizada 7 Feb 2018

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% PCA-based Fault Detection
%
% Inputs: z0 [N x 2] = training data
% z1 [N x 2] = test data
% where: N = number of samples
%
% This code visualizes how PCA can account
% for multivariate data in fault detection.
% It also uses MATLAB's ksdensity for
% estimating the data PDF, so as to compute
% a T^2-based upper control limit.
%
% simpledata.mat has sample temperature [K]
% and concentration [mol/L] data from
% the contents of a simulated CSTR.
%
% The output are plots of the raw data,
% normalized data, and PCA projected data.
% Also, rings representing the T^2-based
% upper control limits at different user-
% defined confidence levels are plotted.
%
% You can edit confidence limits at Line 77.
%
% This code is intended for educational purposes.
%
% Load simpledata.mat and run the following:
% >> pcabased_fault_detection(train,test)

Citar como

Karl Ezra Pilario (2022). PCA-based Fault Detection for 2D Multivariate Process Data (https://www.mathworks.com/matlabcentral/fileexchange/65983-pca-based-fault-detection-for-2d-multivariate-process-data), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2017a
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