PCA-based Fault Detection for 2D Multivariate Process Data

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

545 descargas

Actualizada 7 Feb 2018

Ver licencia

% 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
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!