Nowadays, the concept of “digital twin” has received great attention from both academia and industry. However, few methodological solutions have been reported in existing studies. This paper presents a life prediction method for aircraft structure, and illustrates how this method can be embedded into a “digital twin” framework. This method can fuse heterogeneous information acquired from inspected physic entity, fifinite element software, historical database and predictive model, giving an accurate and real-time prediction of remaining useful life (RUL) for aircraft structure. In the operation of this method, the degradation behaviour of inspected structure is observed in an online manner. Historical record document is used for generating prior knowledge. The external load condition is fed into fifinite element software for calculating the stress intensity factor. The well-known Paris law is adopted as predictive model. Finally, the Bayesian inference is used to integrate the information and predict the future degradation of inspected structure. Theoretical deviation and experiment on a public database demonstrate the effectiveness of this method, facilitating the implementation of “digital twin” in real-world scenario.
How to Cite
digital twin, life prediction, Bayesian inference
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.