View Video Presentation: https://doi.org/10.2514/6.2023-3550.vid Remaining useful life prognostics, maintenance planning and prognostics and health management are crucial instruments to increase the safety, reliability, and efficiency of operational aircraft. These forecasting instruments often depend on in-flight datasets obtained from sensor-based structural health monitoring or condition monitoring of aircraft components to predict structural component states. However, data-driven prognostic instruments are facing the persistent challenge of a deficiency of run-to-failure datasets for aerospace components, which satisfy both quantitative and qualitative data requirements. Degradation models are able to simulate the degradation processes of structural components and thus satisfy the requirements of data-based prediction models in terms of run-to-failure data quantity, quality, and heterogeneity. Since aerospace components are affected by a large number of external factors during operational use, the generation of degradation data with high accuracy is a major challenge in degradation modeling. This study offers a thorough analysis of current degradation modeling techniques adapted to aircraft structures. The objective of this paper is to present an overview of existing degradation modeling approaches in the aeronautical domain through a systematic literature review, as well as to emphasize the advantages, practical applications, and limitations of various methodologies. The present approaches are classified into three categories: data-driven methodologies, physics-based and hybrid degradation modeling approaches.
No takes yet. Share an insight, caveat, or question.
Jilke et al. (2023) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: