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@article{168121,
        author = {Sreehari Kambrath and Immanuel Selvakumar},
        title = {Machine Learning Applications in Aircraft Structural Health Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {1331-1340},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168121},
        abstract = {Structural Health Monitoring (SHM) of aircraft is one of the sophisticated technologies applied that ensures aircraft’s serviceability, safety, and reliability. Traditional SHM methods rely on physical models and expert judgement to identify and assess damage. The traditional methods can consume more time and can be expensive. They may be unable to diagnose and detect certain types of damage. Machine learning (ML) is a powerful tool that can be used to automate and improve the accuracy of SHM. ML has emerged as a promising approach for automating the diagnostic and prognostic process of structural internal and external damages in aircraft, leading to improved maintenance practices and enhanced operational safety. This paper depicts the overall findings and challenges involved in SHM, discusses various ML algorithms and methodologies employed in this field, and presents case studies highlighting the effectiveness of ML techniques in detecting and predicting structural defects. The paper also discusses the scientific application of machine learning processes to identify and rectify structural defects and challenges in any aircraft. We shall discuss the different types of ML algorithms that can be facilitated by SHM and some examples of how ML has been applied to manage and improve the health of aircraft.},
        keywords = {ML, SHM, Reliability, Fatigue, Predictive Maintenance},
        month = {September},
        }