Statistical Techniques For Monitoring Industrial Processes
 

Extract Process Health Status From Data
What you'll learn
Gain proficiency in developing process monitoring solutions for complex industrial systems using popular statistical techniques
Learn how to implement automated fault diagnosis techniques for multivariate systems
Understand the pros and cons of different monitoring techniques for univariate and multivariate process systems
Work on industrial-scale case studies to consolidate the subject-matter understanding
Confidently build statistical process monitoring (SPM) solutions
Requirements
No Python programming and machine learning experience needed. The course covers everything that you need to know to implement the covered statistical process monitoring techniques.
Description
Welcome to your course on Statistical Techniques for Monitoring Industrial Processes where you will learn about the mainstream univariate and multivariate statistical techniques that have proven useful over the years for health monitoring of complex process plants. 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.What will you learn?In this course, you will get step-by-step guidance for developing industrial level solutions for statistical process monitoring. Emphasis is placed on conceptual understanding and practical implementations. Specifically, you will: learn about univariate SPM where you want to monitor a single process variable and multivariate SPM where you want to monitor multiple variables that interact with each otherin addition to covering the conceptual and implementation details, you will undertake several case-studies where you employ the learnt techniques on industrial-scale systems. You will work with data obtained from actual and/or simulated stirred tank reactors, catalytic cracking units, furnaces, chemical plants, polymer reactorsOutcome of the courseOnce you have mastered these techniques, you will be able to handle the monitoring needs of majority of the industrial processes. PrerequisitesWe will not assume any prior Python programming experience. Section 2 of the the course provides a quick introduction to Python programming and the development environment. Also, no prior machine learning experience is required.
Who this course is for
Process data scientists who are looking to learn about statistical process monitoring
Students of chemical engineering, process systems engineering, and process data science
Process industry professionals (process engineers, reliability engineers, plant performance managers, etc.) who are interested in data science and interested in deploying automated monitoring tools for their processes
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