FG
F.C. Gul
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Predicting the remaining useful life (RUL) of composite structures is particularly challenging in impact-damaged carbon fiber–reinforced polymers (CFRPs) under compressive fatigue, where delamination growth with complex morphology and stochastic progression often governs failure. Guided wave–based structural health monitoring (GW-SHM) enables sensitive damage characterization, yet RUL prediction remains difficult due to the strong dependence of GW–delamination interactions on excitation frequency and damage geometry. Physics-based models often struggle to generalize beyond specific configurations, whereas purely data-driven models can capture complex patterns but typically lack consistency with the underlying physical mechanisms. This study introduces a multi-level, frequency-aware prognostic framework that combines the adaptability of deep learning with the physical interpretability of engineered features. GW signals acquired at multiple excitation frequencies are transformed into time- and time–frequency representations, while damage indicators are derived through temporal segmentation. These indicators are correlated with delamination growth measured by C-scan inspections, providing a link between signal-derived features and physical damage evolution. The multi-level architecture integrates convolutional neural networks, multilayer perceptrons, and long short-term memory layers to capture complementary aspects of degradation. Experimental assessment on seven specimens demonstrates that the proposed framework achieves a minimum mean absolute percentage error (MAPE) of 1.904, corresponding to 11% and 55% improvements over the highest- and lowest-performing single-frequency baselines at 160 kHz and 100 kHz, respectively. The results confirm that integrating GW signal processing with deep learning yields robust and physically consistent RUL predictions for impact-damaged CFRPs, while enhancing the interpretability of prognostic outcomes.
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Predicting the remaining useful life (RUL) of composite structures is particularly challenging in impact-damaged carbon fiber–reinforced polymers (CFRPs) under compressive fatigue, where delamination growth with complex morphology and stochastic progression often governs failure. Guided wave–based structural health monitoring (GW-SHM) enables sensitive damage characterization, yet RUL prediction remains difficult due to the strong dependence of GW–delamination interactions on excitation frequency and damage geometry. Physics-based models often struggle to generalize beyond specific configurations, whereas purely data-driven models can capture complex patterns but typically lack consistency with the underlying physical mechanisms. This study introduces a multi-level, frequency-aware prognostic framework that combines the adaptability of deep learning with the physical interpretability of engineered features. GW signals acquired at multiple excitation frequencies are transformed into time- and time–frequency representations, while damage indicators are derived through temporal segmentation. These indicators are correlated with delamination growth measured by C-scan inspections, providing a link between signal-derived features and physical damage evolution. The multi-level architecture integrates convolutional neural networks, multilayer perceptrons, and long short-term memory layers to capture complementary aspects of degradation. Experimental assessment on seven specimens demonstrates that the proposed framework achieves a minimum mean absolute percentage error (MAPE) of 1.904, corresponding to 11% and 55% improvements over the highest- and lowest-performing single-frequency baselines at 160 kHz and 100 kHz, respectively. The results confirm that integrating GW signal processing with deep learning yields robust and physically consistent RUL predictions for impact-damaged CFRPs, while enhancing the interpretability of prognostic outcomes.
Carbon Fiber Reinforced Polymers (CFRPs) are increasingly employed in aerospace applications due to their high strength-to-weight ratio and contribution to fuel efficiency and reducing emissions. Yet, their vulnerability to complex damage modes, particularly impact-induced delamination, presents critical challenges for structural integrity and airworthiness. Delamination often initiates beneath the surface and evolves under cyclic loading in ways that remain difficult to detect and predict. Addressing this challenge, this thesis develops and validates advanced prognostic methodologies for monitoring delamination progression and predicting the remaining useful life (RUL) of CFRP structures under compressive fatigue loading.
A dedicated experimental campaign was designed, beginning with low-velocity impact testing of woven CFRP specimens, followed by compression–compression fatigue. Two active sensing-based structural health monitoring (SHM) techniques—Guided Waves (GW) and Electromechanical Impedance (EMI)—were utilized. Ultrasonic C-scan inspections were used to label delamination growth, enabling a direct correlation between sensor-derived information and physical damage evolution.
Building on this foundation, the thesis first examines the diagnostic capabilities of GW- and EMI-based indicators through advanced signal processing, establishing their sensitivity to progressive delamination and their relevance for life prediction. The research then explores how integrating the two sensing modalities enhances prognostic accuracy, showing that their complementary nature improves robustness across varying impact severities and fatigue regimes. A further line of investigation focuses on GW-based frameworks that explicitly link delamination size to fatigue life. One framework analyzes the sensitivity of individual sensor–actuator paths, providing spatial insight into delamination growth, while the other develops specimen-level correlations that generalize across different configurations. Finally, a multi-level deep learning approach is introduced, where raw GW signals, transformed components, and engineered features are processed in parallel. Embedding domain knowledge into the model architecture improves predictive accuracy, generalization under diverse loading conditions, and explainability of delamination-driven failure mechanisms.
Overall, this work demonstrates that active sensing techniques with advanced AI-driven models substantially enhance the ability to monitor and predict degradation in composite structures. The contributions provide both methodological advances in SHM and practical pathways toward safer, more efficient, and sustainable aerospace operations by mitigating the hidden risks of delamination-induced failure.
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A dedicated experimental campaign was designed, beginning with low-velocity impact testing of woven CFRP specimens, followed by compression–compression fatigue. Two active sensing-based structural health monitoring (SHM) techniques—Guided Waves (GW) and Electromechanical Impedance (EMI)—were utilized. Ultrasonic C-scan inspections were used to label delamination growth, enabling a direct correlation between sensor-derived information and physical damage evolution.
Building on this foundation, the thesis first examines the diagnostic capabilities of GW- and EMI-based indicators through advanced signal processing, establishing their sensitivity to progressive delamination and their relevance for life prediction. The research then explores how integrating the two sensing modalities enhances prognostic accuracy, showing that their complementary nature improves robustness across varying impact severities and fatigue regimes. A further line of investigation focuses on GW-based frameworks that explicitly link delamination size to fatigue life. One framework analyzes the sensitivity of individual sensor–actuator paths, providing spatial insight into delamination growth, while the other develops specimen-level correlations that generalize across different configurations. Finally, a multi-level deep learning approach is introduced, where raw GW signals, transformed components, and engineered features are processed in parallel. Embedding domain knowledge into the model architecture improves predictive accuracy, generalization under diverse loading conditions, and explainability of delamination-driven failure mechanisms.
Overall, this work demonstrates that active sensing techniques with advanced AI-driven models substantially enhance the ability to monitor and predict degradation in composite structures. The contributions provide both methodological advances in SHM and practical pathways toward safer, more efficient, and sustainable aerospace operations by mitigating the hidden risks of delamination-induced failure.
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Carbon Fiber Reinforced Polymers (CFRPs) are increasingly employed in aerospace applications due to their high strength-to-weight ratio and contribution to fuel efficiency and reducing emissions. Yet, their vulnerability to complex damage modes, particularly impact-induced delamination, presents critical challenges for structural integrity and airworthiness. Delamination often initiates beneath the surface and evolves under cyclic loading in ways that remain difficult to detect and predict. Addressing this challenge, this thesis develops and validates advanced prognostic methodologies for monitoring delamination progression and predicting the remaining useful life (RUL) of CFRP structures under compressive fatigue loading.
A dedicated experimental campaign was designed, beginning with low-velocity impact testing of woven CFRP specimens, followed by compression–compression fatigue. Two active sensing-based structural health monitoring (SHM) techniques—Guided Waves (GW) and Electromechanical Impedance (EMI)—were utilized. Ultrasonic C-scan inspections were used to label delamination growth, enabling a direct correlation between sensor-derived information and physical damage evolution.
Building on this foundation, the thesis first examines the diagnostic capabilities of GW- and EMI-based indicators through advanced signal processing, establishing their sensitivity to progressive delamination and their relevance for life prediction. The research then explores how integrating the two sensing modalities enhances prognostic accuracy, showing that their complementary nature improves robustness across varying impact severities and fatigue regimes. A further line of investigation focuses on GW-based frameworks that explicitly link delamination size to fatigue life. One framework analyzes the sensitivity of individual sensor–actuator paths, providing spatial insight into delamination growth, while the other develops specimen-level correlations that generalize across different configurations. Finally, a multi-level deep learning approach is introduced, where raw GW signals, transformed components, and engineered features are processed in parallel. Embedding domain knowledge into the model architecture improves predictive accuracy, generalization under diverse loading conditions, and explainability of delamination-driven failure mechanisms.
Overall, this work demonstrates that active sensing techniques with advanced AI-driven models substantially enhance the ability to monitor and predict degradation in composite structures. The contributions provide both methodological advances in SHM and practical pathways toward safer, more efficient, and sustainable aerospace operations by mitigating the hidden risks of delamination-induced failure.
A dedicated experimental campaign was designed, beginning with low-velocity impact testing of woven CFRP specimens, followed by compression–compression fatigue. Two active sensing-based structural health monitoring (SHM) techniques—Guided Waves (GW) and Electromechanical Impedance (EMI)—were utilized. Ultrasonic C-scan inspections were used to label delamination growth, enabling a direct correlation between sensor-derived information and physical damage evolution.
Building on this foundation, the thesis first examines the diagnostic capabilities of GW- and EMI-based indicators through advanced signal processing, establishing their sensitivity to progressive delamination and their relevance for life prediction. The research then explores how integrating the two sensing modalities enhances prognostic accuracy, showing that their complementary nature improves robustness across varying impact severities and fatigue regimes. A further line of investigation focuses on GW-based frameworks that explicitly link delamination size to fatigue life. One framework analyzes the sensitivity of individual sensor–actuator paths, providing spatial insight into delamination growth, while the other develops specimen-level correlations that generalize across different configurations. Finally, a multi-level deep learning approach is introduced, where raw GW signals, transformed components, and engineered features are processed in parallel. Embedding domain knowledge into the model architecture improves predictive accuracy, generalization under diverse loading conditions, and explainability of delamination-driven failure mechanisms.
Overall, this work demonstrates that active sensing techniques with advanced AI-driven models substantially enhance the ability to monitor and predict degradation in composite structures. The contributions provide both methodological advances in SHM and practical pathways toward safer, more efficient, and sustainable aerospace operations by mitigating the hidden risks of delamination-induced failure.
In aircraft composite structures, impact-induced delamination poses a significant threat to their integrity, necessitating meticulous inspections to ensure reliable operation. However, monitoring delamination growth with existing nondestructive methods remains challenging due to the intricate nature of the damage mechanisms involved. This study introduces a novel approach by integrating guided waves (GWs) and electromechanical impedance (EMI) to achieve the prediction of remaining useful life (RUL) in woven-type carbon fiber-reinforced polymer (CFRP) plate-like structures subjected to compression fatigue conditions following a low-velocity impact. The novelty of this work lies in the fusion of GW and EMI techniques for the prediction of RUL, which is integrated into a comprehensive prognostic framework. Damage indicators (DIs) derived from GW and EMI measurements were first analyzed for their correlation with measured delamination growth and then used as inputs for prognostic models developed using deep neural networks. This approach significantly enhances the accuracy and reliability of RUL predictions as the proposed GW–EMI fusion models aim to harness the most effective predictions from each DI. An evaluation of the DIs revealed that GW–DIs achieved better accuracy on average across all cycles compared to EMI–DIs. Both fusion models demonstrated strong accuracy for individual samples, with Fusion Model 1 (RUL-fus-1) showing a 12% improvement and Fusion Model 2 (RUL-fus-2) showing a 24% improvement across all cycles on average. Notably, Fusion Model 2 exhibited the lowest error in the final cycles, with a 48% improvement in accuracy compared to the least successful model, demonstrating its potential for more precise prognosis through the integration of GW-DIs and EMI-DIs.
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In aircraft composite structures, impact-induced delamination poses a significant threat to their integrity, necessitating meticulous inspections to ensure reliable operation. However, monitoring delamination growth with existing nondestructive methods remains challenging due to the intricate nature of the damage mechanisms involved. This study introduces a novel approach by integrating guided waves (GWs) and electromechanical impedance (EMI) to achieve the prediction of remaining useful life (RUL) in woven-type carbon fiber-reinforced polymer (CFRP) plate-like structures subjected to compression fatigue conditions following a low-velocity impact. The novelty of this work lies in the fusion of GW and EMI techniques for the prediction of RUL, which is integrated into a comprehensive prognostic framework. Damage indicators (DIs) derived from GW and EMI measurements were first analyzed for their correlation with measured delamination growth and then used as inputs for prognostic models developed using deep neural networks. This approach significantly enhances the accuracy and reliability of RUL predictions as the proposed GW–EMI fusion models aim to harness the most effective predictions from each DI. An evaluation of the DIs revealed that GW–DIs achieved better accuracy on average across all cycles compared to EMI–DIs. Both fusion models demonstrated strong accuracy for individual samples, with Fusion Model 1 (RUL-fus-1) showing a 12% improvement and Fusion Model 2 (RUL-fus-2) showing a 24% improvement across all cycles on average. Notably, Fusion Model 2 exhibited the lowest error in the final cycles, with a 48% improvement in accuracy compared to the least successful model, demonstrating its potential for more precise prognosis through the integration of GW-DIs and EMI-DIs.
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.
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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.
Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI-driven approach, the Hilbert transform-convolutional neural network under a semi-supervised learning paradigm, to designing reliable HIs (fulfilling requirements, referred to as 'fitness'). It exclusively utilizes current guided wave data, eliminating the need for historical information. Ensemble learning techniques were also used to enhance HI quality while reducing deep learning randomness. The fitness equation is refined for dependable comparisons and practicality. The methodology is validated through investigations on T-single stiffener CFRP panels under compression-fatigue and dogbone CFRP specimens under tension-fatigue loadings, showing high performance of up to 93% and 81%, respectively, in prognostic criteria.
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Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI-driven approach, the Hilbert transform-convolutional neural network under a semi-supervised learning paradigm, to designing reliable HIs (fulfilling requirements, referred to as 'fitness'). It exclusively utilizes current guided wave data, eliminating the need for historical information. Ensemble learning techniques were also used to enhance HI quality while reducing deep learning randomness. The fitness equation is refined for dependable comparisons and practicality. The methodology is validated through investigations on T-single stiffener CFRP panels under compression-fatigue and dogbone CFRP specimens under tension-fatigue loadings, showing high performance of up to 93% and 81%, respectively, in prognostic criteria.
A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage accumulation process during operational conditions. Additionally, designing a HI that is independent of historical SHM data (i.e., from the healthy state until the current state) is even more challenging as HI and remaining useful life prediction are time-dependent phenomena. A reliable SHM technique is required to extract informative time-independent data, and a powerful model is necessary to construct a proper HI from that data. The lamb wave (LW) technique is a useful SHM method that can extract such time-independent data. However, translating the LW data at each time step to the appropriate HI value
is a challenge. AI—deep learning in this case—offers significant mathematical potential to meet this difficulty. A semi-supervised learning approach is developed to train the model using the simulated ideal HIs as the targets. The model uses the current LW data, without prior or subsequent data, to output the current HI value. Prognostic criteria are then calculated using the entire HI trajectory until the end-of-life. To validate the proposed approach, aging experiments from NASA’s prognostics data repository are used, which include composite specimens subjected to a tension-tension fatigue loading and monitored using the LW technique. The LW data is first processed using the Hilbert transform, and then, upper and lower signal envelopes in two states – baseline and current time – are used to feed the deep learning model. The results confirm the effectiveness of the proposed approach according to the prognostic criteria. The effect of different triggering frequencies of the LW system on the results is also discussed in terms of the prognostic criteria.
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A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage accumulation process during operational conditions. Additionally, designing a HI that is independent of historical SHM data (i.e., from the healthy state until the current state) is even more challenging as HI and remaining useful life prediction are time-dependent phenomena. A reliable SHM technique is required to extract informative time-independent data, and a powerful model is necessary to construct a proper HI from that data. The lamb wave (LW) technique is a useful SHM method that can extract such time-independent data. However, translating the LW data at each time step to the appropriate HI value
is a challenge. AI—deep learning in this case—offers significant mathematical potential to meet this difficulty. A semi-supervised learning approach is developed to train the model using the simulated ideal HIs as the targets. The model uses the current LW data, without prior or subsequent data, to output the current HI value. Prognostic criteria are then calculated using the entire HI trajectory until the end-of-life. To validate the proposed approach, aging experiments from NASA’s prognostics data repository are used, which include composite specimens subjected to a tension-tension fatigue loading and monitored using the LW technique. The LW data is first processed using the Hilbert transform, and then, upper and lower signal envelopes in two states – baseline and current time – are used to feed the deep learning model. The results confirm the effectiveness of the proposed approach according to the prognostic criteria. The effect of different triggering frequencies of the LW system on the results is also discussed in terms of the prognostic criteria.
Conference paper
(2023)
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F.C. Gul, M. Moradi, R. Benedictus, RAFIK HADJRIA, YEVGENIYA LUGOVTSOVA, D. Zarouchas
Under in-plane compressive load conditions, the growth of a delamination initially induced by an impact can be followed by a fast growth after a threshold level, which leads to a catastrophic failure in composite structures. To avoid reaching this critical level, it is essential to uncover the delamination size and growth pattern in real time. Ultrasonic Guided Waves (UGW) have a strong capability to interrogate and monitor the structure in real-time and thus track the growth of damage, which may occur during the flight cycles. Although various types of damage affect the monitored UGW signals, it is challenging to determine from the UGW signals what types of damage and at what rate of growth are occurring within the structure. UGW signals can be acquired at defined intervals and then analysed to possibly detect different types of damages, such as delamination, and to quantify the rate of damage growth over fatigue cycles. However, correlating the UGW-based Damage Indicators (DIs) with the specific type of damage, such as delamination, and damage growth is a challenging task as the relation between these DIs and the actual damage state is very complex. Therefore, in this study, a supervised Deep Neural Network-based (DNN) prediction model is proposed aiming to diagnose the delamination size of the composite structure by correlating the UGW-based DIs with the quantified time-varying delamination size. UGW data is collected through a network of permanently installed piezoelectric transducers (PZTs). The delamination size is obtained through ultrasonic C-Scan technique at defined cycles. DIs are extracted in time, frequency, and time-frequency domains and used as the input for the DNN-based regression model. Each sensor-actuator path is considered as an independent set of indicators, which are separated for training, validation, and testing purposes. The effect of the different paths on the delamination size prediction is presented along with the model performance on measured delamination growth in woven type composite sample.
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Under in-plane compressive load conditions, the growth of a delamination initially induced by an impact can be followed by a fast growth after a threshold level, which leads to a catastrophic failure in composite structures. To avoid reaching this critical level, it is essential to uncover the delamination size and growth pattern in real time. Ultrasonic Guided Waves (UGW) have a strong capability to interrogate and monitor the structure in real-time and thus track the growth of damage, which may occur during the flight cycles. Although various types of damage affect the monitored UGW signals, it is challenging to determine from the UGW signals what types of damage and at what rate of growth are occurring within the structure. UGW signals can be acquired at defined intervals and then analysed to possibly detect different types of damages, such as delamination, and to quantify the rate of damage growth over fatigue cycles. However, correlating the UGW-based Damage Indicators (DIs) with the specific type of damage, such as delamination, and damage growth is a challenging task as the relation between these DIs and the actual damage state is very complex. Therefore, in this study, a supervised Deep Neural Network-based (DNN) prediction model is proposed aiming to diagnose the delamination size of the composite structure by correlating the UGW-based DIs with the quantified time-varying delamination size. UGW data is collected through a network of permanently installed piezoelectric transducers (PZTs). The delamination size is obtained through ultrasonic C-Scan technique at defined cycles. DIs are extracted in time, frequency, and time-frequency domains and used as the input for the DNN-based regression model. Each sensor-actuator path is considered as an independent set of indicators, which are separated for training, validation, and testing purposes. The effect of the different paths on the delamination size prediction is presented along with the model performance on measured delamination growth in woven type composite sample.