Part 3: Phase II: Exploiting the Model

Irawen Fault Detection and Diagnosis in Chemical and Petrochemical Processes

You can detect the faults using the Squared Prediction Error (SPE) and T2 control charts. Next, learn how to use contribution plots in conjunction with these control charts for fault diagnosis. You can also simulate several faults and validate the performance of the PCA model against complex process upsets. Then watch industrial success stories in fault detection and diagnosis with the MATLAB® product family.

Additional Resources: - MATLAB for the Chemical and Petrochemical Industry: https://bit.ly/2Mxc91a - MATLAB and Simulink for Predictive Maintenance: https://bit.ly/3opdXqt - MATLAB for Machine Learning: https://bit.ly/2YlIQRY - A Benchmark Software for MSPC: https://bit.ly/2KR2GRZ