Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance (eBook, PDF)
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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 r...
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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