Knowledge-Guided AI for Industrial Asset Health Monitoring

Modern industrial systems generate vast streams of sensor data that capture the behavior and health of complex physical assets. While machine learning has become a powerful tool for analyzing such data, purely data-driven approaches often struggle with limited labeled examples, lack of interpretability, and poor generalization to rare or previously unseen failure conditions. This talk explores how scientific knowledge-guided machine learning can address these challenges by embedding established engineering knowledge, such as reliability principles and failure analysis, directly into learning frameworks for industrial asset health monitoring. By treating domain knowledge and data as complementary sources of supervision, knowledge-guided approaches enable models to reason about assets, sensors, and failure mechanisms in a way that is explainable, data-efficient, and robust to distribution shifts. Through examples from industrial monitoring, the talk highlights a broader vision of KGML as a bridge between machine learning and engineering science.