If a manufacturing company's main goal is to sell products profitably, protecting production systems from defects is essential and has led to vast documentation and expert knowledge. Industry 4.0 has facilitated access to sensor and operational data across the shop floor, enabling data-driven models that detect faults and predict failures, which are crucial for predictive maintenance to minimize unplanned downtimes and costs. Commonly, a universally applicable machine learning approach is used without explicitly integrating prior knowledge from sources beyond training data, risking incorrect rediscovery or neglecting already existing knowledge. This book explores how to integrate knowledge graphs with neural networks for similarity-based failure prediction, anomaly detection and diagnosis to improve predictions while reducing the number of learnable parameters and failure examples.

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About the Author

Patrick Klein worked as a research assistant at the University of Trier and also briefly in part-time for the Trier branch of the German Research Center for Artificial Intelligence (DFKI) while conducting his doctoral research at the University's Internet of Things Laboratory, focusing on combining expert knowledge with deep learning for predictive maintenance. After completing his PhD thesis, he joined the Predictive Service Center in the R&D department of the technology and global market leader in machine tools, as a Data Scientist/Engineer, developing data-driven solutions for predictive maintenance.