| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Cheng, Liangliang
University of Groningen
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (15/15 displayed)
- 2023Vibration-Based Quality Assessment of Metallic Turbine Blades Considering Measurement Uncertainty
- 2023Integrated interval Mahalanobis classification system for the quality classification of turbine blades based on vibrational data incorporating measurement uncertainty
- 2022An ensemble classifier for vibration-based quality monitoringcitations
- 2022A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detectioncitations
- 2022CNN-DST: Ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognitioncitations
- 2021Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification systemcitations
- 2021Vibrational quality classification of metallic turbine blades under measurement uncertainty
- 2021Vibrational quality classification of metallic turbine blades under measurement uncertainty
- 2021CNN-DST: ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognition
- 2021Mahalanobis classification system (MCS) integrated with binary particle swarm optimization for robust quality classification of complex metallic turbine bladescitations
- 2020Mahalonobis classification system for quality classification of complex metallic turbine blades
- 2020An ensemble classifier for vibration-based quality monitoring
- 2020Mahalanobis classification system for quality classification of complex metallic turbine blades
- 2020Classifier fusion for vibrational NDT of complex metallic turbine blades
- 2020On the Influence of Reference Mahalanobis Distance Space for Quality Classification of Complex Metal Parts Using Vibrationscitations
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document
Classifier fusion for vibrational NDT of complex metallic turbine blades
Abstract
Parts with geometrical complexity bring significant challenges in nondestructive testing (NDT). The Process Compensated Resonance Testing (PCRT) method has recently shown promising results for inspecting complex-shaped metallic parts. PCRT is a broadband vibrational testing procedure that relies on the extraction of resonant frequencies coupled to advanced learning methods. Once a suitable classifier is determined, it is then applied to unknown test samples in order to classify them as healthy/defected. The target of this work is to upgrade the PCRT with an advanced classifier to increase the classification performance. For this purpose, first the inclusion of the Q-factors to the available PCRT feature set i.e., only frequencies, is investigated and then, a novel classifier fusion based on Dempster-Shafer theory of evidence (DST) has been proposed to combine several constituent models. The constituent models are selected to be adaptively boosted NNs (ABNNs) trained by using different numbers of features. The proposed algorithm(ABNN + DST) is applied to polycrystalline Nickel alloy first-stage turbine blades with complex geometry. The results indicate that the proposed DST-based fusion algorithm increase the classification accuracy from 93.5% to 96.5%.