Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review

A systematic literature review of 212 studies finds that Physics-Informed Machine Learning (PIML) improves predictive performance in Prognostics and Health Management (PHM) across all four classification classes, but the field is skewed toward lithium-ion batteries and bearings and dominated by problem-specific solutions. The review, published on arXiv (arXiv:2608.10047v1), introduces a four-class scheme (observational bias, inductive bias, learning bias, hybrid approaches) and calls for future research on transferable design patterns, benchmarks, and uncertainty-aware models for online deployment.

arXiv:2608.10047v1 Announce Type: new Abstract: In modern industry, keeping complex systems reliable, safe, and efficient hinges on Prognostics and Health Management PHM . Machine Learning ML has largely driven advancements in diagnostics and prognostics, yet purely data-driven models face inherent limitations, such as poor generalization, an inability to infer causal relationships, and a lack of interpretability. Physics-Informed Machine Learning PIML helps mitigate these limitations by incorporating prior physical knowledge directly into the ML pipeline, thereby fostering growing interest in its application to PHM. This work investigates how PIML is being leveraged in the context of PHM through a systematic literature review of 212 studies. The review introduces a four-class classification scheme, consisting of observational bias, inductive bias, learning bias, and hybrid approaches, and further categorizes studies by PHM task. Across all four classes, the reviewed studies consistently demonstrate improved predictive performance over conventional baselines across a broad range of assets, although the literature is heavily skewed toward lithium-ion batteries and bearings, and dominated by problem-specific solutions. Overall, the review indicates that physics-informed approaches already provide tangible benefits, whereas claims of improvements concerning some of the aforementioned limitations lack sufficient supporting evidence. Future research should prioritize transferable design patterns, benchmarks comparing integration strategies, and uncertainty-aware models that are lightweight and robust enough for online deployment in real-world settings.