| 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
Mahalanobis classification system for quality classification of complex metallic turbine blades
Abstract
The complex geometry of metallic components combined with the variety of possible damage features limits the application of conventional NDT technologies. For parts with complex geometric shapes relevant product quality assurance tools are needed. Process Compensated Resonance Testing (PCRT) is an advanced and sensitive non-destructive evaluation method. It employs Mahalanobis Taguchi System (MTS) to classify the components as Good/Bad by evaluating the variations on resonance frequencies in Mahalanobis space. However, the process of feature selection and threshold determination in MTS is questionable. In the present paper, a two-stage Mahalanobis Classification System (MCS) approach is proposed coupled with binary particle swarm optimization procedure. The proposed MCS approach is applied to equiaxed Nickel alloy first-stage turbine blades with various possible defects. The obtained results demonstrate the high classification accuracy and evidence of the superior performance of the proposed approach.