Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance
https://DevCourseWeb.com
English | 2024 | ISBN: NA | 361 Pages | PDF | 17.9 MB
This book provides a guided tour of ML techniques utilized in process industry for plant health management. Step-by-step instructions, supported with industrial-scale process datasets, show how to develop ML-based solutions for equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions.
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.
