• Proposes a hierarchical framework for Knowledge Driven Machine Learning (KDML) in PHM, integrating scientific paradigms, knowledge sources, representations, and embedding methods.
• Demonstrates through case studies that embedding domain knowledge (inductive experience, physical models, signal processing) enhances generalization and interpretability of ML models in PHM.
• Highlights the critical need for interpretability to ensure trustworthy deployment of AI in PHM, addressing challenges of limited generalization and weak interpretability in current ML approaches.
• Provides a roadmap and usage recommendations for KDML, discussing challenges and potential applications in the PHM domain.