Digital twin-driven maintenance and fault detection for modern aircraft

Abstract

The growing intricacy of contemporary aircraft, with its tightly interconnected mechanical, electrical, and cyber-physical components, poses substantial difficulties for traditional aircraft maintenance and fault detection methods. Traditional reactive, scheduled, and standalone condition-based maintenance methods are insufficient for detecting early faults, modeling system deterioration, and offering precise prognostics in dynamic operational scenarios. This study explores how Digital Twin technology enhances the efficiency of intelligent aircraft maintenance and fault detection.

A digital twin Is a real-time, highly accurate virtual model of an aircraft or its components, incorporating live sensor data, past maintenance logs, environmental factors, physics-based simulations, and data-driven insights. The study envisions a layered Digital Twin framework, incorporating data gathering and networking, data cleansing and amalgamation, hybrid physics-AI analytics, virtual system simulation, and advisory layers. Sophisticated methodologies, including multi-physics simulations, deep learning-based time-series analysis, anomaly detection techniques, and remaining useful life (RUL) estimations, are utilized to pinpoint discrepancies between anticipated and actual system dynamics, facilitating proactive fault detection and predictive maintenance strategies.

Studies examining engine health monitoring, structural health assessment, and avionics diagnostics from prominent aerospace companies are analysed to assess the impact of Digital Twin-driven maintenance. The findings indicate enhanced fault detection precision, decreased unexpected stoppages, optimized maintenance schedules, and improved operational stability. Despite obstacles such as data scalability, cybersecurity, model fidelity, and compatibility with existing fleets, the results indicate that Digital Twin technology provides a scalable and intelligent framework for advanced aircraft maintenance systems.