Code repository for the online course 'Statistical Techniques for Monitoring Industrial Processes'
https://www.udemy.com/course/statistical-techniques-for-monitoring-industrial-processes/
The course on 'Statistical Techniques for Monitoring Industrial Processes' covers the mainstream univariate and multivariate statistical techniques that have proven useful over the years for health monitoring of complex process plants. In this course, you will put the concepts learnt into practice using process industry-relevant datasets. Modern industrial plants are complex and therefore, it is a no-brainer that plant monitoring is an essential activity. Without exaggeration, it can be said that 24X7 monitoring of process performance and plant equipment health status, and forecast of impending failures are no longer a ‘nice to have’ but an absolute necessity! This course will equip you with the tools necessary to develop process monitoring solutions that includes both the fault detection (is the process or a signal behaving abnormally?) and fault diagnosis (which variables are behaving abnormally) components.
Why study SPM (statistical process monitoring)?
While artificial neural networks and deep learning grab most of the limelight now-a-days, classical statistical approaches are still are the bedrock of industrial process monitoring and enjoy immense popularity. Compared to neural network models, multivariate statistical techniques like PCA (principal component analysis) and PLS (partial least squares) are simpler to understand, more interpretable, and easier to develop and maintain; several successful stories. and give you equal if not better performance than very complex models.