TY - GEN
T1 - Acoustic emission characterisation of two pre-cracked specimens
AU - Gálvez, Antonio
AU - Galar, Diego
AU - Alonso, Asier
AU - Errasti-Alcalá, Borja
AU - Bienvenido, Ismael
AU - Ortego, Patxi
AU - Juuso, Esko
N1 - Publisher Copyright:
© 2022 18th International Conference on Condition Monitoring and Asset Management, CM 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - This article contains the experiments carried-out to study the capabilities of Acoustic Emissions (AE) in a Ship To Shore (STS) crane. This solution studies the implementation of Structural Health Monitoring (SHM) in an STS crane based on acoustic emissions (AE) technique for detecting cracks and assessing their growth in steel elements subjected to fatigue. The first experiment is performed using a compact tension specimen (CT) made of steel S355 whose dimensions are 125x120x50 mm and its cracks and dimensions are defined based on ASTM and ISO standards. The CT is monitored using AE sensors, and then, the features are extracted from the raw data and used to train, test and validate an unsupervised model. The crack detection model obtains a remarkable accuracy; crack detection at sizing of 3 mm length. As the CT dimensions are small, it is difficult to evaluate the attenuation of AE signals, which is completely necessary for monitoring STS cranes. Therefore, a second experiment is performed using a panel made of steel S355, whose dimensions are 2120x200x8 mm; the panel contains a crack of 50x3 mm. This experiment is performed to analyse the AE signals that come from cracks; specifically, to assess signals attenuation, how the attenuation affects cracks detection in the panel, and features evolution while crack propagation. This is led by monitoring the crack growth with crack detection gauges and installing the AE sensors at different distances of the crack. The assessment is used to develop an unsupervised model to detect cracks and an algorithm for localizing them.
AB - This article contains the experiments carried-out to study the capabilities of Acoustic Emissions (AE) in a Ship To Shore (STS) crane. This solution studies the implementation of Structural Health Monitoring (SHM) in an STS crane based on acoustic emissions (AE) technique for detecting cracks and assessing their growth in steel elements subjected to fatigue. The first experiment is performed using a compact tension specimen (CT) made of steel S355 whose dimensions are 125x120x50 mm and its cracks and dimensions are defined based on ASTM and ISO standards. The CT is monitored using AE sensors, and then, the features are extracted from the raw data and used to train, test and validate an unsupervised model. The crack detection model obtains a remarkable accuracy; crack detection at sizing of 3 mm length. As the CT dimensions are small, it is difficult to evaluate the attenuation of AE signals, which is completely necessary for monitoring STS cranes. Therefore, a second experiment is performed using a panel made of steel S355, whose dimensions are 2120x200x8 mm; the panel contains a crack of 50x3 mm. This experiment is performed to analyse the AE signals that come from cracks; specifically, to assess signals attenuation, how the attenuation affects cracks detection in the panel, and features evolution while crack propagation. This is led by monitoring the crack growth with crack detection gauges and installing the AE sensors at different distances of the crack. The assessment is used to develop an unsupervised model to detect cracks and an algorithm for localizing them.
UR - http://www.scopus.com/inward/record.url?scp=85145879972&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85145879972
T3 - 18th International Conference on Condition Monitoring and Asset Management, CM 2022
SP - 87
EP - 110
BT - 18th International Conference on Condition Monitoring and Asset Management, CM 2022
PB - British Institute of Non-Destructive Testing
T2 - 18th International Conference on Condition Monitoring and Asset Management, CM 2022
Y2 - 7 June 2022 through 9 June 2022
ER -