MFL sensing and pattern recognition based automated damage detection for steel chain NDE (Conference Presentation)

2019 ◽  
Author(s):  
Ju-Won Kim ◽  
Byoungjoon Yu ◽  
Minsoo Park ◽  
Seunghee Park
Keyword(s):  
Pattern Recognition ◽  
Damage Detection

Real-time damage detection based on pattern recognition

2016 ◽  
Vol 17 (3) ◽  
pp. 338-354 ◽  
Author(s):  
João Pedro de Oliveira Dias Prudente dos Santo ◽  
Christian Crémona ◽  
António Paulo Campos da Silveira ◽  
Luís Calado de Oliveira Martins
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Real Time

scholarly journals A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 211
Author(s):  
Susheel Dharmadhikari ◽  
Chandrachur Bhattacharya ◽  
Asok Ray ◽  
Amrita Basak
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Damage Detection ◽  
Fatigue Damage ◽  
Early Stage ◽  
Confocal Microscope ◽  
Recognition Algorithm ◽  
Pattern Recognition Algorithm ◽  
Notched Specimens ◽  
Digital Microscope

The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors and, thereafter, tested on an MTS apparatus integrated with a confocal microscope and a digital microscope. The confocal microscope is focused on the notch root of the specimens, whereas the digital microscope is focused on the side of the notch. Two features, viz., the crack opening displacement (COD) and the crack length, are extracted during the tests in addition to the ultrasonic signal data. These signal data are analyzed using a machine learning framework that is built upon a symbolic time-series algorithm. This framework is interrogated for crack detection in the crack coalescence (CC) regime defined by COD of ~3 μm and detected through the confocal microscope. Additionally, the framework is probed in the crack propagation (CP) regime characterized by a crack length of ~0.2 mm and detected via the digital microscope. For the CC regime, training accuracies of 79.82% and 81.94% are achieved, whereas testing accuracies of 68.18% and 74.12% are observed for the U- and V-notched specimens, respectively. For the CP regime, overall training accuracies of 88.3% and 91.85% are observed, and accordingly, testing accuracies of 81.94% and 85.62% are obtained for the U- and V-notched specimens, respectively. The results show that a combined machine learning and pattern recognition algorithm enables robust and reliable fatigue damage detection in aerospace structural components.

The application of statistical pattern recognition methods for damage detection to field data

2008 ◽  
Vol 17 (6) ◽  
pp. 065023 ◽  
Author(s):  
A Cheung ◽  
C Cabrera ◽  
P Sarabandi ◽  
K K Nair ◽  
A Kiremidjian ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Damage Detection ◽  
Field Data ◽  
Statistical Pattern Recognition ◽  
Pattern Recognition Methods ◽  
Statistical Pattern
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