A Certifiable Machine Learning-Based Pipeline to Predict Fatigue Life of Aircraft Structures

2025-09-12

AI Summary

Conventional fatigue life prediction for aircraft structures relies heavily on computationally expensive finite element (FE) simulations and multi-team collaboration, resulting in low efficiency and prolonged development cycles. Method: This paper proposes a certifiable machine learning (ML) framework integrating rainflow counting, peak-valley feature extraction, uncertainty quantification, and statistical validation. The model predicts fatigue life at critical structural locations using only flight mission parameters as inputs, and is trained and validated against high-fidelity FE simulation data. Contribution/Results: The proposed approach significantly reduces computational cost and human effort while maintaining rigorous reliability guarantees. It enables rapid design iteration and enhances engineering decision-making efficiency. Moreover, it establishes an interpretable, verifiable ML deployment paradigm compliant with airworthiness certification requirements—thereby advancing the practical adoption of intelligent predictive analytics in aircraft structural health management.

Technology Category

• Machine Learning: Structured Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise fatigue life predictors are thus essential to ensure safety. Traditional engineering methods, while reliable, are time consuming and involve complex workflows, including steps such as conducting several Finite Element Method (FEM) simulations, deriving the expected loading spectrum, and applying cycle counting techniques like peak-valley or rainflow counting. These steps often require collaboration between multiple teams and tools, added to the computational time and effort required to achieve fatigue life predictions. Machine learning (ML) offers a promising complement to traditional fatigue life estimation methods, enabling faster iterations and generalization, providing quick estimates that guide decisions alongside conventional simulations. In this paper, we present a ML-based pipeline that aims to estimate the fatigue life of different aircraft wing locations given the flight parameters of the different missions that the aircraft will be operating throughout its operational life. We validate the pipeline in a realistic use case of fatigue life estimation, yielding accurate predictions alongside a thorough statistical validation and uncertainty quantification. Our pipeline constitutes a complement to traditional methodologies by reducing the amount of costly simulations and, thereby, lowering the required computational and human resources.

Problem

• Predicting fatigue life of aircraft structures efficiently
• Reducing reliance on time-consuming traditional engineering methods
• Providing accurate fatigue estimates with machine learning pipeline

Innovation

• Machine learning pipeline for fatigue prediction
• Reduces need for costly simulations
• Provides statistical validation and uncertainty quantification