Nondestructive inspection of a lead rubber bearing using ultrasonic wave

Yoneda, Yutaka; Mita, Akira

doi:10.1117/12.658943

Cited by 5 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The micro‐cracks can be evaluated by comparing AE parameters between failed bearings and new bearings. Yoneda and Mita 19 obtained the internal state of rubber bearings using active sensing method. An ultrasonic wave of 1 MHz is used to detect whether a lead rod existed inside a rubber bearing.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method to monitor axial pressure and shear deformation of rubber bearings under coupled compression and shear loading

Zeng

Pan

2023

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

Laminated rubber bearings are the key components of base isolated structures. It is important to effectively monitor the axial pressure and shear deformation in rubber bearings. In previous studies, the axial pressure and shear deformation of rubber bearings have been monitored separately using wavelet‐packet‐based method. However, when the axial pressure and deformation of rubber bearings change at the same time, the prediction error of the above method is large. Therefore, this paper proposed a new deep neural network architecture, named P&D‐NET to monitor the axial pressure and shear deformation simultaneously. The P&D‐NET combines wavelet packet decomposition method and fully connected neural network. In this study, the coupling effects of axial pressure and shear deformation monitoring are studied by full‐scale experiments. The details of P&D‐NET are also introduced. Three full‐scale smart rubber bearings (SRB) were tested under different axial pressure and shear deformation to form a real measured dataset. The influence of feature extraction methods, network structures, and weighting coefficient in loss function was investigated to optimize the network. By using the optimal hyperparameters, the root‐mean‐square error (RMSE) of axial pressure prediction and shear deformation prediction in the test set is 1.41 MPa and 17.6%, which has reduced the error by up to 68.8% compared to the previous wavelet‐packet‐based method.

show abstract

Section: Introductionmentioning

confidence: 99%

A deep learning method to monitor axial pressure and shear deformation of rubber bearings under coupled compression and shear loading

Zeng

Pan

2023

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to introduce new technology to detect rubber aging. Yoneda and Mita 19 used an ultrasonic wave of 1 MHz to detect whether a lead rod existed inside a rubber bearing. This active sensing method provided a new idea for the detection of rubber bearing damage.…”

Section: Introductionmentioning

confidence: 99%

Combination of active sensing method and data-driven approach for rubber aging detection

Zeng,

Chen,

Xiong

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

Rubber bearings are key components of base-isolated structures, and the monitoring of their damage states is an important task. Aging is a primary concern affecting the service life and isolation effect of rubber bearings. Therefore, this study combined an active sensing method and a data-driven approach to detect rubber aging. A shear stiffness, accelerated aging, and active sensing experiments were conducted on a scaled rubber specimen. As the aging level increased, the shear stiffness of the specimens gradually increased from 116.69 to 127.82 N/mm, but this change was not linear. Due to variations in the degree of aging, discrepancies may arise in the time and frequency domain characteristics of detection signals. However, establishing an empirical relationship between the degree of aging and the features of detection signals were highly challenging. A deep-learning-based data-driven method was used to predict the aging level and shear stiffness using detection signals. The deep learning model successfully detected the aging level, and the prediction accuracy on the validation and test sets reached 99.98%. For the deep learning model for aging level prediction, the optimal input vector length is 4096, the recommended number of layers is 3–5, and the recommended number of cells in each layer is 256–2048. Moreover, the deep learning model also detected the shear stiffness of the rubber specimen. The mean absolute error was 0.27 N/mm on the validation set and 0.28 N/mm on the test set. For the deep learning model for shear stiffness prediction, the optimal input vector length is 4096, and the optimal structure is seven layers with 2048 cells in each layer.

show abstract

Shear-sensing characteristics and theoretical model analysis of multiwalled carbon nanotube/natural rubber composite

Liu,

Qi,

Liao

et al. 2025

Composites Communications

View full text Add to dashboard Cite

Nondestructive inspection of a lead rubber bearing using ultrasonic wave

Cited by 5 publications

References 1 publication

A deep learning method to monitor axial pressure and shear deformation of rubber bearings under coupled compression and shear loading

A deep learning method to monitor axial pressure and shear deformation of rubber bearings under coupled compression and shear loading

Combination of active sensing method and data-driven approach for rubber aging detection

Shear-sensing characteristics and theoretical model analysis of multiwalled carbon nanotube/natural rubber composite

Contact Info

Product

Resources

About