Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance

Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance

By Ankur Kumar & Jesus Flores-Cerrillo

This book provides a practical and application-driven introduction to using machine learning in Python for industrial process monitoring, equipment condition monitoring, and predictive maintenance. It is designed for engineers, data scientists, and practitioners who want to move from raw industrial data to actionable insights that improve reliability and operational performance.

The authors bridge the gap between theory and practice by combining fundamental concepts, real-world industrial use cases, and hands-on Python implementations. Readers are guided through the complete workflow: data exploration, feature extraction, model development, validation, and deployment-oriented thinking for industrial environments.

The book covers a wide range of techniques, including:

• Statistical and multivariate monitoring methods (such as PCA, PLS, and control charts)

• Classical machine learning algorithms for fault detection and diagnosis

• Neural networks and advanced models for condition monitoring

• Vibration and time-series data analysis

• Remaining Useful Life (RUL) estimation and predictive maintenance strategies

With a strong focus on real industrial data and practical challenges, this book equips readers with the tools needed to design and implement effective monitoring and predictive maintenance solutions using Python.

Who this book is for

• Process, mechanical, and reliability engineers

• Data scientists working with industrial or sensor data

• Professionals interested in predictive maintenance and Industry 4.0 applications

• Readers with basic knowledge of Python and machine learning who want real-world applications

  Size: A4(20*28cm)

  Printing: 80 gm – color

  Cover: Softcover