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About the Book

This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions in process industry. The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems.

This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications.

Broadly, the book covers the following:

• Exploratory analysis of process data
• Best practices for process monitoring and predictive maintenance solutions
• Univariate monitoring via control charts and time series data mining
• Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.)
• Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes
• Fault detection and diagnosis of rotating machinery using vibration data
• Remaining useful life predictions for predictive maintenance

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About the Authors

Ankur Kumar

Ankur Kumar holds a PhD degree (2016) in Process Systems Engineering from the University of Texas at Austin and a bachelor’s degree (2012) in Chemical Engineering from the Indian Institute of Technology Bombay. He currently works at Linde in the Advanced Digital Technologies & Systems Group in Linde’s Center of Excellence, where he has developed several in-house machine learning-based monitoring and process control solutions for Linde’s hydrogen and air-separation plants. Ankur’s tools have won several awards both within and outside Linde. One of his tools, PlantWatch (a plantwide fault detection and diagnosis tool), received the 2021 Industry 4.0 Award by the Confederation of Industry of the Czech Republic. Ankur has authored or co-authored several peer-reviewed journal papers (in the areas of data-driven process modeling and monitoring), is a frequent reviewer for many top-ranked Journals, and has served as Session Chair at several international conferences. Ankur served as an Associate Editor of the Journal of Process Control from 2019 to 2021. Most recently, he was included in the ‘Engineering Leaders Under 40, Class of 2023‘ by Plant Engineering Magazine.

Jesus Flores Cerrillo

Jesus Flores-Cerrillo is currently an Associate Director - R&D at Linde and manages the Advanced Digital Technologies & Systems Group in Linde’s Center of Excellence. He has over 20 years of experience in the development and implementation of monitoring technologies and advanced process control & optimization solutions. Jesus holds a PhD degree in Chemical Engineering from McMaster University and has authored or co-authored more than 40 peer-reviewed journal papers in the areas of multivariate statistics and advanced process control among others. His team develops and implements novel plant monitoring, machine learning, IIOT solutions to improve the efficiency and reliability of Linde’s processes. Jesus’s team received the Artificial Intelligence and Advanced Analytics Leadership 2020 award from the National Association of Manufacturers’ Manufacturing Leadership Council.