Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review

Azad, Muhammad Muzammil; Kim, Sungjun; Cheon, Yongsung; Kim, Heung-Soo

doi:10.1080/09243046.2023.2215474

Cited by 11 publications

(3 citation statements)

References 148 publications

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“…These models share a comparable architecture and perform operations across various layers. The essential elements of convolution-based models encompass the convolutional layer, pooling layer, activation layer, batch normalization layer, dropout layer, and global pooling layer [74,100].…”

Section: Machine Learning Approaches In Sphmmentioning

confidence: 99%

“…Advancements in AI have boosted data-driven techniques, which are thus being continuously adopted in aircraft SPHM. For intelligent SPHM, both machine learning (ML) and deep learning (DL) approaches have gained popularity [74]; therefore, this section focuses on the latest data-driven technologies adopted for the SPHM of aircraft structures. In the context of SPHM, a significant trend emerges-the abundance of healthy data compared to damaged data.…”

Section: Introduction To Data-driven Approachesmentioning

confidence: 99%

See 1 more Smart Citation

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

Khalid,

Song,

Azad

et al. 2023

Mathematics

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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.

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Section: Machine Learning Approaches In Sphmmentioning

confidence: 99%

Section: Introduction To Data-driven Approachesmentioning

confidence: 99%

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

Khalid,

Song,

Azad

et al. 2023

Mathematics

Self Cite

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“…To avoid structural failure and severe loss, it is necessary to quickly detect damage in composite structures to prolong their service life. PHM technology provides early damage detection and helps avoid the deterioration of various industrial systems [7][8][9][10][11][12]. Recently, techniques for PHM based on Machine Learning (ML) and Deep Learning (DL) using vibration signals have been continuously adopted for fault diagnosis in various structures.…”

Section: Introductionmentioning

confidence: 99%

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

Kim,

Azad,

Song

et al. 2023

Applied Sciences

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As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of failure data and unpredictable failure cases. Despite numerous studies conducted to address this limitation, including techniques like data augmentation and transfer learning, significant challenges remain. In this study, the Wasserstein Generative Adversarial Network (WGAN) model using a time-series data augmentation technique is proposed as a solution to the data imbalance problem. To ensure the performance of the WGAN model, time-series data augmentation of experimental data is executed with a frequency analysis. After that, a One-Dimensional Convolutional Neural Network (1D CNN) is used for fault diagnosis in laminated composites, validating the performance improvement after data augmentation. The proposed data augmentation significantly elevated the performance of the 1D CNN classification model compared to its non-augmented counterpart. Specifically, the accuracy increased from 89.20% to 91.96%. The precision improved remarkably from 29.76% to 74.10%, and its sensitivity rose from 33.33% to 94.39%. Collectively, these enhancements highlight the vital role of data augmentation in improving fault diagnosis performance.

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An explainable artificial intelligence‐based approach for reliable damage detection in polymer composite structures using deep learning

Azad,

Kim

2024

Polymer Composites

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Artificial intelligence (AI) techniques are increasingly used for structural health monitoring (SHM) of polymer composite structures. However, to be confident in the trustworthiness of AI models, the models must be reliable, interpretable, and explainable. The use of explainable artificial intelligence (XAI) is critical to ensure that the AI model is transparent in the decision‐making process and that the predictions it provides can be trusted and understood by users. However, existing SHM methods for polymer composite structures lack explainability and transparency, and therefore reliable damage detection. Therefore, an interpretable deep learning model based on an explainable vision transformer (X‐ViT) is proposed for the SHM of composites, leading to improved repair planning, maintenance, and performance. The proposed approach has been validated on carbon fiber reinforced polymers (CFRP) composites with multiple health states. The X‐ViT model exhibited better damage detection performance compared to existing popular methods. Moreover, the X‐ViT approach effectively highlighted the area of interest related to the prediction of each health condition in composites through the patch attention aggregation process, emphasizing their influence on the decision‐making process. Consequently, integrating the ViT‐based explainable deep‐learning model into the SHM of polymer composites provided improved diagnostics along with increased transparency and reliability.Highlights Autonomous damage detection of polymer composites using vision transformer based deep learning model. Explainable artificial intelligence by highlighting region of interest using patch attention. Comparison with the existing state of the art structural health monitoring methods.

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Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review

Cited by 11 publications

References 148 publications

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

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

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

An explainable artificial intelligence‐based approach for reliable damage detection in polymer composite structures using deep learning

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