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.

翻译：本文全面综述了物理信息机器学习（PIML）技术在状态监测领域中的应用。PIML的核心思想是将已知物理定律与约束融入机器学习算法，使其既能从现有数据中学习，又能保持与物理原理的一致性。通过融合领域知识与数据驱动学习，PIML方法相比纯数据驱动方法具有更高的准确性和可解释性。本综述详细考察了将已知物理原理集成到机器学习框架中的具体方法，以及这些方法在状态监测特定任务中的适用性。物理知识融入机器学习模型可通过多种方式实现，每种方式各有其独特优势与不足。本文详细阐述了每种物理知识集成方法在数据驱动模型中的独特优势与局限性，综合考虑了计算效率、模型可解释性以及对不同状态监测与故障检测系统的泛化能力等因素。通过多个案例研究和相关文献，展示了PIML在状态监测应用中的有效性。从所综述的文献中，可以证明PIML在状态监测中的多功能性与潜力。新型PIML方法为应对状态监测的复杂性及相关挑战提供了创新解决方案。本全面综述为该领域的未来研究奠定了基础。随着技术持续进步，PIML有望在提升工程系统的维护策略、系统可靠性和整体运行效率方面发挥关键作用。