Data-driven and physics-based prognostics for accurate fatigue crack prediction

Prognostics and Health Management (PHM) has become an essential discipline for ensuring the reliability and safety of complex engineering systems. Accurate prediction of the remaining useful life (RUL) of critical components allows for proactive maintenance, minimizing unplanned downtime and extending the operational lifespan of assets. This paper presents a comprehensive review of both data-driven and physics-based prognostics methods, offering practical guidance for selecting the most appropriate approach based on specific application needs.Data-driven methods, such as Neural Networks (NN) and Gaussian Process (GP) regression, leverage historical data to model and predict future system states. These methods are particularly effective when large datasets are available and the underlying physical processes are not well understood. However, their performance can degrade in the presence of high noise levels or limited data. In contrast, physics-based methods, including Particle Filter (PF) and Bayesian Inference, rely on mathematical models derived from the physical laws governing the system. These methods are more robust to noise and can provide more accurate long-term predictions, especially when the physical processes are well characterized.A case study on fatigue crack growth is used to illustrate the strengths and limitations of each approach. The results show that while data-driven methods excel in computational efficiency and are easy to implement, physics-based methods offer superior accuracy and robustness in complex, dynamic environments. The paper concludes with practical recommendations for selecting prognostics methods based on factors such as data availability, model complexity, and computational resources. This review aims to equip engineers and researchers with the knowledge needed to make informed decisions when implementing prognostics strategies in their fields of application.