Machine Learning for Process Industry Series: Books 1 and 3
$60.00
Bought separately
$24.99
Minimum price
$35.00
Suggested price

Machine Learning for Process Industry Series: Books 1 and 3

About the Bundle

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. 

  • Share this bundle
  • Categories

    • 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

    Readers

  • 354

    Pages

  • 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

    Pages

  • 100%

    Complete

  • 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.

The Leanpub 60 Day 100% Happiness Guarantee

Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.

Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.

You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!

So, there's no reason not to click the Add to Cart button, is there?

See full terms...

80% Royalties. Earn $16 on a $20 book.

We pay 80% royalties. That's not a typo: you earn $16 on a $20 sale. If we sell 5000 non-refunded copies of your book or course for $20, you'll earn $80,000.

(Yes, some authors have already earned much more than that on Leanpub.)

In fact, authors have earnedover $13 millionwriting, publishing and selling on Leanpub.

Learn more about writing on Leanpub

Free Updates. DRM Free.

If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).

Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.

Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.

Learn more about Leanpub's ebook formats and where to read them

Write and Publish on Leanpub

You can use Leanpub to easily write, publish and sell in-progress and completed ebooks and online courses!

Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks.

Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. (Or, if you are producing your ebook your own way, you can even upload your own PDF and/or EPUB files and then publish with one click!) It really is that easy.

Learn more about writing on Leanpub