Advanced Structural Health Monitoring and Damage Detection System for Aircraft: Integration of Cutting-Edge Sensors, Machine Learning, and Digital Twin Technology

The aviation industry demands high standards of safety, reliability, and efficiency. This study presents an advanced Structural Health Monitoring (SHM) system for aircraft, integrating state-of-the-art sensors, machine learning (ML) algorithms, and digital twin technology. The proposed system was evaluated through laboratory and real-world scenarios, achieving a detection accuracy of ۹۸%, a damage localization error of <۵ mm, and reducing unscheduled downtime by ۳۰% compared to traditional methods. Fiber optic sensors demonstrated a strain resolution of ±۰.۰۲ microstrain, effectively detecting delamination in composite materials with ۹۵% accuracy. Piezoelectric transducers identified cracks and corrosion in metallic components with ۹۷% accuracy, while acoustic emission sensors localized stress waves associated with crack growth with a margin of error of <۵ mm. Machine learning algorithms, particularly deep neural networks, achieved superior performance with ۹۶% accuracy, outperforming traditional models such as support vector machines (۹۱%) and random forests (۹۴%). Digital twin technology provided real-time damage visualization and improved predictive maintenance capabilities, resulting in ۲۰% higher accuracy in fatigue threshold predictions. The findings demonstrate the transformative potential of the proposed SHM system to enhance aviation safety, reduce maintenance costs, and optimize operational efficiency. These advancements set a new benchmark for monitoring and maintaining modern aircraft structures.

کلیدواژه ها:

Structural Health Monitoring (SHM) ، Damage Detection ، Aircraft Safety ، Machine Learning ، Digital Twin Technology ، Predictive Maintenance ، Fiber Optic Sensors ، Aviation Efficiency