Ferda C. Gul’s scientific contributions

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Publications (2)


Fig. 1. Composite dogbone specimens under T-T fatigue (left) and single T-stiffener CFRP panel under C–C fatigue (right) being monitored by PZT sensors (red circles) (dimensions in [mm]).
Fig. 2. The overall proposed framework: (a) guided wave (GW) monitoring; (b) signal processing (Hilbert transform); (c) base learner model (SSCNN); (d) ensemble learner model; (e) extracted health indicator (only shown for single T-stiffener CFRP panels).
Fig. 3. Right column: (a) Sensed GW signals excited by 150 kHz and (b) their envelopes for all 36 paths of the NASA dataset (Layup 1) at baseline and cycle 60000, as well as the relevant (c) 3D input of SSCNN at cycle 60000. Left column: Their 2D display for only one path.
Fig. 4. The architecture of the semi-supervised convolutional neural network as the base learner.
Fig. 8. HIs obtained by the proposed methodology with WAE-Fitness for different datasets, given all frequency inputs.

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A novel machine learning model to design historical-independent health indicators for composite structures
  • Article

February 2024

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101 Reads

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8 Citations

Composites Part B Engineering

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Ferda C. Gul

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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.


ADVANCED HEALTH MONITORING OF COMPOSITE STRUCTURES THROUGH DEEP LEARNING-BASED ANALYSIS OF LAMB WAVE DATA FOR DEVELOPING HEALTH INDICATORS

September 2023

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17 Reads

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1 Citation

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Ferda C. Gul

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Juan Chiachio

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[...]

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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.

Citations (1)


... In recent years, data-driven SHM methods have been widely studied [7][8][9][10][11][12][13][14]. These methods directly establish mapping relationships between detected signals and damage states based on their correlations, making them a popular choice for SHM applications due to their straightforward implementation [15][16][17]. ...

Reference:

Variational Neural Network Embedded with Digital Twins for Probabilistic Structural Damage Quantification
A novel machine learning model to design historical-independent health indicators for composite structures
  • Citing Article
  • February 2024

Composites Part B Engineering