A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

Abstract

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

Aircraft Structural Design and Life-Cycle Assessment through Digital Twins

“…An example of this approach was given in [60] concerning the propagation of fatigue cracks in aluminum alloy fuselages, where cycles correspond to pressurization in flight and depressurization when taxiing. Khalid et al [61] gave a thorough review of sensing and structural health monitoring of aeronautical structures. Using digital twins, proactive maintenance planning can be used by detecting and predicting potential structural issues before they escalate, aligning well with the principles of damage-tolerant design by allowing timely interventions to mitigate further damage.…”

Digital twin-driven prognostics and health management for industrial assets

“…Cui et al 7 overviewed theoretical concepts related to DT-PHM studies for electromechanical products. Khalid et al 8 further reviewed data-based and model-based approaches and explored their correlations in DT-PHM applications for aircraft structure. Yüce et al 9 provided a technical overview of the role of machine learning techniques in the DT-driven PHM methods for wind energy infrastructure.…”

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

“…As noted in recent discussions by professional organizations such as Capgemini and AWS, by mid-life, a large passenger aircraft can amass hundreds of thousands of documents, underscoring the critical role of sophisticated data management strategies for lifecycle optimization platforms with additional capabilities for image recognition and analysis of aviation data [76]. By applying complex algorithms and AI, it is possible to predict potential system failures before they occur, thus allowing for timely interventions that can prevent costly downtimes and enhance aircraft performance and safety [77,78].…”