Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring

Condition monitoring plays a vital role in ensuring the reliability and optimal performance of various engineering systems. Traditional methods for condition monitoring rely on physics-based models and statistical analysis techniques. However, these approaches often face challenges in dealing with complex systems and the limited availability of accurate physical models. In recent years, physics-informed machine learning (PIML) has emerged as a promising approach for condition monitoring, combining the strengths of physics-based modelling and data-driven machine learning. This study presents a comprehensive overview of PIML techniques in the context of condition monitoring. The central concept driving PIML is…

• MU
  McMaster University
• NS
  Natural Sciences and Engineering Research Council of Canada