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

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Yi Zeng, Tengsheng Chen, Feng Xiong, Kailai Deng, Yuanqing Xu
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引用次数: 0

Abstract

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.
主动感知与数据驱动相结合的橡胶老化检测方法
橡胶支座是基础隔震结构的关键部件,其损伤状态监测是一项重要任务。老化是影响橡胶轴承使用寿命和隔离效果的主要问题。因此,本研究将主动感知方法与数据驱动方法相结合,对橡胶老化进行检测。对橡胶试件进行了剪切刚度、加速老化和主动感知实验。随着时效水平的增加,试件的抗剪刚度从116.69 N/mm逐渐增加到127.82 N/mm,但这种变化不是线性的。由于老化程度的不同,检测信号的时域和频域特性可能会出现差异。然而,建立老化程度与检测信号特征之间的经验关系是极具挑战性的。采用基于深度学习的数据驱动方法,利用检测信号预测老化程度和抗剪刚度。深度学习模型成功地检测了老化程度,在验证集和测试集上的预测准确率达到99.98%。对于老化水平预测的深度学习模型,最优输入向量长度为4096,推荐层数为3-5,每层推荐细胞数为256-2048。此外,深度学习模型还检测了橡胶试件的剪切刚度。验证集的平均绝对误差为0.27 N/mm,测试集的平均绝对误差为0.28 N/mm。对于剪切刚度预测的深度学习模型,最优输入向量长度为4096,最优结构为7层,每层2048个单元。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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