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

Salman Khalid, Jinwoo Song, Muhammad Muzammil Azad, Muhammad Umar Elahi, Jaehun Lee, Soo Ho Jo, Heung Soo Kim

Research output: Contribution to journalReview articlepeer-review

17 Scopus citations

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.

Original languageEnglish
Article number3837
JournalMathematics
Volume11
Issue number18
DOIs
StatePublished - Sep 2023

Keywords

  • aircraft maintenance
  • data-driven approaches
  • digital twin technology
  • health management
  • model-based approaches
  • structural prognostics

Fingerprint

Dive into the research topics of 'A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management'. Together they form a unique fingerprint.

Cite this