Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance
Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance
English | 2025 | ISBN: 3658469854 | 641 Pages | PDF EPUB (True) | 37 MB
English | 2025 | ISBN: 3658469854 | 641 Pages | PDF EPUB (True) | 37 MB
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 (ML) approach is used without explicitly integrating prior knowledge from sources beyond training data, risking incorrect rediscovery or neglecting already existing knowledge. Integrating expert knowledge with ML can address the scarcity of failure examples and avoid the learning of spurious correlations, though it poses technical challenges when combining Semantic Web-based knowledge graphs with neural networks (NNs) for time series data.