Fault Detection and Diagnosis in Manufacturing Multivariate Analysis | Part 1

Fault detection and diagnosis are widely used in manufacturing analytics both in predictive maintenance and optimization. The key element of fault detection and diagnosis is from multivariate analysis such as Principal Component Analysis (PCA). This is uniquely suited for the analysis and interpretation of process data because it exploits the correlations amongst variables in the data sets.
Learn about the application of fault detection and diagnosis and industry success stories with MATLAB.
Watch the full series on Fault Detection and Diagnosis in Chemical and Petrochemical Processes: • Fault Detection and Di...
Additional Resources:
- MATLAB for the Chemical and Petrochemical Industry: bit.ly/3UyYNma
- MATLAB and Simulink for Predictive Maintenance: bit.ly/2Tp2yLq
- MATLAB for Machine Learning: bit.ly/2YlIQRY
- A Benchmark Software for MSPC: bit.ly/2KR2GRZ
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[@rudheeshrk]

Very Good Explanation....

[@andrestherock1]

it's a good explanation the only suggestion is if you can put subtitles because my listening is not the best and I lost in some sentences.

[@SamRao]

Subtitles are available on the video on the MathWorks website.
www.mathworks.com/videos/series/fault-detection-and-diagnosis-in-chemical-and-petrochemical-processes.html