An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading
The prognostic of the Remaining Useful Life (RUL) of composite structures remains a critical challenge as it involves understanding complex degradation behaviors while it is emerging for maintaining the safety and reliability of aerospace structures. As damage accumulation is the primary degradation...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-10-01
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| Series: | Composites Part C: Open Access |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666682024001002 |
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| author | Ferda C. Gül Morteza Moradi Dimitrios Zarouchas |
| author_facet | Ferda C. Gül Morteza Moradi Dimitrios Zarouchas |
| author_sort | Ferda C. Gül |
| collection | DOAJ |
| description | The prognostic of the Remaining Useful Life (RUL) of composite structures remains a critical challenge as it involves understanding complex degradation behaviors while it is emerging for maintaining the safety and reliability of aerospace structures. As damage accumulation is the primary degradation indicator from the structural integrity point of view, a methodology that enables monitoring the damage mechanisms contributing to the structure's failure may facilitate a reliable and effective RUL prognosis. Therefore, in this study, an integrated methodology has been introduced by targeting the RUL and progressive delamination state via Deep Neural Network (DNN) trained with Guided wave-based damage indicators (GW-DIs). These GW-DIs are obtained via signal processing, Hilbert transform, and Continuous Wavelet Transform. This work uses GW-DIs to train and test the proposed model within two frameworks: one focusing on individual sample analysis to explore path dependency in RUL and delamination prognosis and another on an ensembled dataset to propose a generic model across varying stress scenarios. Results from the study indicate that proposed DNN frameworks are capable of encapsulating fast and slow degradation scenarios to evaluate the RUL prediction with associated delamination progress, which could contribute to ensuring the integrity and longevity of critical life-safe structures. |
| format | Article |
| id | doaj-art-9851c26f2c0d428bb97ac1e70fdce12a |
| institution | Kabale University |
| issn | 2666-6820 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Composites Part C: Open Access |
| spelling | doaj-art-9851c26f2c0d428bb97ac1e70fdce12a2024-12-09T04:28:20ZengElsevierComposites Part C: Open Access2666-68202024-10-0115100531An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loadingFerda C. Gül0Morteza Moradi1Dimitrios Zarouchas2Center of Excellence in Artificial Intelligence for Structures, Prognostic and Health Management, Department of Aerospace Structures and Materials, Faculty of Aerospace Engineering, Delft University of Technology, the NetherlandsCenter of Excellence in Artificial Intelligence for Structures, Prognostic and Health Management, Department of Aerospace Structures and Materials, Faculty of Aerospace Engineering, Delft University of Technology, the NetherlandsCenter of Excellence in Artificial Intelligence for Structures, Prognostic and Health Management, Department of Aerospace Structures and Materials, Faculty of Aerospace Engineering, Delft University of Technology, the NetherlandsThe prognostic of the Remaining Useful Life (RUL) of composite structures remains a critical challenge as it involves understanding complex degradation behaviors while it is emerging for maintaining the safety and reliability of aerospace structures. As damage accumulation is the primary degradation indicator from the structural integrity point of view, a methodology that enables monitoring the damage mechanisms contributing to the structure's failure may facilitate a reliable and effective RUL prognosis. Therefore, in this study, an integrated methodology has been introduced by targeting the RUL and progressive delamination state via Deep Neural Network (DNN) trained with Guided wave-based damage indicators (GW-DIs). These GW-DIs are obtained via signal processing, Hilbert transform, and Continuous Wavelet Transform. This work uses GW-DIs to train and test the proposed model within two frameworks: one focusing on individual sample analysis to explore path dependency in RUL and delamination prognosis and another on an ensembled dataset to propose a generic model across varying stress scenarios. Results from the study indicate that proposed DNN frameworks are capable of encapsulating fast and slow degradation scenarios to evaluate the RUL prediction with associated delamination progress, which could contribute to ensuring the integrity and longevity of critical life-safe structures.http://www.sciencedirect.com/science/article/pii/S2666682024001002Compressive fatigueImpact damageStructural Health MonitoringGuided wavesRemaining Useful LifeDeep learning |
| spellingShingle | Ferda C. Gül Morteza Moradi Dimitrios Zarouchas An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading Composites Part C: Open Access Compressive fatigue Impact damage Structural Health Monitoring Guided waves Remaining Useful Life Deep learning |
| title | An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading |
| title_full | An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading |
| title_fullStr | An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading |
| title_full_unstemmed | An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading |
| title_short | An integrated approach for prognosis of Remaining Useful Life for composite structures under in-plane compressive fatigue loading |
| title_sort | integrated approach for prognosis of remaining useful life for composite structures under in plane compressive fatigue loading |
| topic | Compressive fatigue Impact damage Structural Health Monitoring Guided waves Remaining Useful Life Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666682024001002 |
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