A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection

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, physicsinformed machine learning (PIML) has emerged as a promising approach for condition monitoring, combining the strengths of physics-based modeling 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 the incorporation of known physical laws and constraints into machine learning algorithms, enabling them to learn from available data while remaining consistent with physical principles.Through fusing domain knowledge with data-driven learning, PIML methods offer enhanced accuracy and interpretability in comparison to purely data-driven approaches.In this comprehensive survey, detailed examinations are performed with regard to the methodology by which known physical principles are integrated within machine learning frameworks, as well as their suitability for specific tasks within condition monitoring.Incorporation of physical knowledge into the ML model may be realized in a variety of methods, with each having its unique advantages and drawbacks.The distinct advantages and limitations of each methodology for the integration of physics within data-driven models are detailed, considering factors such as computational efficiency, model interpretability, and generalizability to different systems in condition monitoring and fault detection.Several case studies and works of literature utilizing this emerging concept are presented to demonstrate the efficacy of PIML in condition monitoring applications.From the literature reviewed, the versatility and potential of PIML in condition monitoring may be demonstrated.Novel PIML methods offer an innovative solution for addressing the complexities of condition monitoring and associated chal-

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