  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
challenges. This comprehensive survey helps form the foundation for future work
in the field. As the technology continues to advance, PIML is expected to play
a crucial role in enhancing maintenance strategies, system reliability, and
overall operational efficiency in engineering systems.
