Machine Learning for Process Industry Series: Books 1 and 3
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Machine Learning for Process Industry Series: Books 1 and 3

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In the 21st century, data science has become an integral part of the work culture at every manufacturing industry and process industry is no exception to this modern phenomenon. From predictive maintenance to process monitoring, fault diagnosis to advanced process control, machine learning-based solutions are being used to achieve higher process reliability and efficiency. However, few books are available that adequately cater to the needs of budding process data scientists. The scant available resources include: 1) generic data science books that fail to account for the specific characteristics and needs of process plants 2) process domain-specific books with rigorous and verbose treatment of underlying mathematical details that become too theoretical for industrial practitioners. Understandably, this leaves a lot to be desired. Books are sought that have process systems in the backdrop, stress application aspects, and provide a guided tour of ML techniques that have proven useful in process industry. This series ‘Machine Learning for Process Industry addresses this gap to reduce the barrier-to-entry for those new to process data science.


The first book of the series ‘Machine Learning in Python for Process Systems Engineering’ covers the basic foundations of machine learning and provides an overview of broad spectrum of ML methods primarily suited for static systems. Step-by-step guidance on building ML solutions for process monitoring, soft sensing, predictive maintenance, etc. are provided using real process datasets. Aspects relevant to process systems such as modeling correlated variables via PCA/PLS, handling outliers in noisy multidimensional dataset, controlling processes using reinforcement learning, etc. are covered. The second book of the series ‘Machine Learning in Python for Dynamic Process Systems’ focuses on dynamic systems and provides a guided tour along the wide range of available dynamic modeling choices. Emphasis is paid to both the classical methods (ARX, CVA, ARMAX, OE, etc.) and modern neural network methods. Applications on time series analysis, noise modeling, system identification, and process fault detection are illustrated with examples. The third book of the series ‘Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance’ takes a deep dive into an important application area of ML, viz, prognostics and health management. ML methods that are widely employed for the different aspects of plant health management, namely, fault detection, fault isolation, fault diagnosis, and fault prognosis, are covered in detail. Emphasis is placed on conceptual understanding and practical implementations. Future books of the series will continue to focus on other aspects and needs of process industry. It is hoped that these books can help process data scientists find innovative ML solutions to the real-world problems faced by the process industry.


With the growing trend in usage of machine learning in the process industry, there is growing demand for process domain experts/process engineers with data science/ML skills. These books have been written to cover the existing gap in ML resources for such process data scientists. Specifically, books of this series will be useful to budding process data scientists, practicing process engineers looking to ‘pick up’ machine learning, and data scientists looking to understand the needs and characteristics of process systems. With the focus on practical guidelines and industrial-scale case studies, we hope that these books lead to wider spread of data science in the process industry. 

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    • Data Science
    • Systems Engineering
    • Python
    • Machine Learning

About the Books

Machine Learning in Python for Process Systems Engineering

Achieve Operational Excellence Using Process Data
  • 123


  • 354


  • PDF

This book provides an application-focused exposition of modern ML tools that have proven useful in process industry and hands-on illustrations on how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, inferential modeling, dimensionality reduction, and process control. This book considers unique characteristics of industrial process data and uses real data from industrial systems for illustrations. With the focus on practical implementation and minimal programming or ML prerequisites, the book covers the gap in available ML resources for industrial practitioners. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning. The readers will find all the resources they need to deal with high-dimensional, correlated, noisy, corrupted, multimode, and nonlinear process data.

The book has been divided into four parts. Part 1 provides a perspective on the importance of ML in process systems engineering and lays down the basic foundations of ML. Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the various characteristics of industrial process systems. Part 3 is focused on artificial neural networks and deep learning. Part 4 covers the important topic of deploying ML solutions over web and shows how to build a production-ready process monitoring web application.

Broadly, the book covers the following:

  • Varied applications of ML in process industry
  • Fundamentals of machine learning workflow
  • Practical methodologies for pre-processing industrial data
  • Classical ML methods and their application for process monitoring, fault diagnosis, and soft sensing
  • Deep learning and its application for predictive maintenance
  • Reinforcement learning and its application for process control
  • Deployment of ML solution over web

If you do not have a PayPal account, you can purchase the book at Google Play.

Machine Learning in Python for Process and Equipment Conditio...

From Data to Process Insights
  • 365


  • 100%


  • PDF

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

If you do not have a PayPal account, you can purchase the book at Google Play.

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