Bildvorhersage zur Schadenserkennung in Motorkomponenten für Zukünftige Flugzyklen

Details

Veröffentlichungs-Nr.

EP4826477

Anmeldetag

30. Januar 2026

Veröffentlichung

21. Oktober 2026

Rechtsraum

EP

IPC

G05B23/02

Abstract

Systems (200) and methods (10, 20) for forecasting damage in engine components using advanced machine learning techniques, including hybrid frameworks and spatiotemporal modeling integrate annotated inspection images (13a) and operational engine data to generate forecasted damage states and operational parameters for future engine cycles. The machine learning model (17) includes convolutional layers to extract spatial features, recurrent layers to analyze temporal patterns, and an output layer that predicts future damage states, operational conditions, and distress ranks of engine components. Physics-informed constraints are incorporated to ensure predictions adhere to physical laws, such as irreversibility of damage progression. By leveraging these forecasts, the system optimizes maintenance schedules, reduces unnecessary inspections, and extends time-on-wing of engine components. This novel approach provides a significant improvement over traditional methods by automating damage analysis, capturing multi-failure mechanisms, and enhancing predictive maintenance strategies.

Anmelder

Firma

General Electric Company

Land

🇺🇸 USA

Fachgebiete