interpretable ai in cmfd

Condition monitoring is pivotal in ensuring the reliability and efficiency of industrial systems, yet traditional diagnostic methods often fall short in offering transparency and actionable insights. this paper explores the integration of interpretable artificial intelligence (ai) techniques for advancing condition monitoring. we propose a framework that combines domain-specific feature engineering with explainable ai models to enhance fault detection, diagnosis, and prognostics across diverse industrial assets. our approach incorporates methods such as shapley additive explanations (shap), local interpretable model-agnostic explanations (lime), and prototype-based learning, enabling stakeholders to comprehend model outputs and gain trust in automated decision-making systems. we validate our framework using real-world datasets from rotating machinery and oil analysis, demonstrating its ability to achieve high diagnostic accuracy while maintaining interpretability. the results highlight the potential of interpretable ai to bridge the gap between complex machine learning models and practical, user-friendly solutions in condition monitoring. by fostering trust and understanding, this work aims to support informed maintenance decisions, mitigate downtime, and promote sustainable operational practices