Data Structures and Algorithms

    

Hybrid Paris Physics-Informed Neural Network for Predicting CFM56-7B Engine Fatigue Failure

Authors: Yebeen Hwang

Existing methodologies for predictive maintenance in aviation engines have largely diverged into data-centric and physics-centric models, each constrained by their unchangeable limitations. In this study, a hybrid framework integrating both perspectives was developed to address fatigue-induced failures in the CFM56-7B engine. Specifically, a Bayesian Physics-Informed Neural Network (B-PINN) was constructed, embedding Paris' Law within a deep learning structure and modeling key fatigue parameters as probabilistic distributions. Selected sensor data from NASA's CMAPSS FD004 dataset was employed to assume latent stress signals and simulate fatigue crack propagation. The results show that the proposed model has advantages on interpretability and reliability of fatigue predictions but also quantifies uncertainty through variational inference and Monte Carlo dropout.

Comments: 26 Pages.

Download: PDF

Submission history

[v1] 2025-07-23 04:50:09

Unique-IP document downloads: 475 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus