Industrial Applications of Machine Learning

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features - Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution - Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors - Includes four case studies addressing real-world industrial problems solved with machine learning techniques - A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving - Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka

Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Esteban Puerto-Santana, Concha Bielza

1 The Fourth Industrial Revolution 2 Machine Learning 3 Applications of Machine Learning in Industrial Sectors 4 Component-Level Case Study: Remaining Useful Life of Bearings 5 Machine-Level Case Study: Fingerprint of Industrial Motors 6 Production-Level Case Study: Automated Visual Inspection of a Laser Process 7 Distribution-Level Case Study: Forecasting of Air Freight Delays