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Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal

机译:使用经过时间反转的改进的振动声调制来监控螺栓的早期松动

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Structural health monitoring (SHM) of bolted joints has played a vital role in estimation of bolt looseness and prediction of residual service life of bolted connections, thus saving money and significantly improving the efficiency of maintenance routines across industries. In the past decades, several SHM methods, particularly acoustic/ultrasonic methods, have been used to identify the health status of bolted connections. Compared to the linear ultrasound techniques such as active sensing, the vibro-acoustic modulation (VAM) method that is based on nonlinear ultrasonic features has proven its efficiency in bolt early looseness monitoring; however, some drawbacks impede its practical use. The main contribution of this paper is to develop a modified VAM (MVAM) that can circumvent existing problems with practical implementation and provide higher sensitivity. First, the shaker used in the traditional VAM was replaced by a piezoceramic transducer to improve its practicality. Moreover, instead of sine waves, linear swept sine signals were used for both low-frequency (LF) pump vibration and high-frequency (HF) probe wave. In other words, no a priori knowledge of the structural condition is needed, which further broadens the scope of application. Subsequently, the time reversal (TR) method was applied to overcome problems including signal energy dissipation and low signal-to-noise ratio (SNR) in traditional VAM. Moreover, the noise-assisted multivariate empirical mode decomposition (NA-MEMD) and multiscale multivariate sample entropy (MMSE) were used to develop a new damage index (DI) for bolt early looseness monitoring. Finally, multiple repeated experiments were conducted to verify the accuracy of the proposed method and its ability to simplify bolt early looseness monitoring in terms of practical operation, by comparing the proposed MMSE-based DI with nonlinear DI of traditional VAM method. Published by Elsevier Ltd.
机译:螺栓连接的结构健康监测(SHM)在估算螺栓松动和预测螺栓连接的剩余使用寿命方面起着至关重要的作用,从而节省了成本,并显着提高了整个行业的日常维护效率。在过去的几十年中,已经使用了几种SHM方法,特别是声学/超声波方法来确定螺栓连接的健康状况。与主动检测等线性超声技术相比,基于非线性超声特征的振动声调制(VAM)方法已证明其在螺栓早期松动监测中的效率;但是,一些缺点阻碍了它的实际使用。本文的主要贡献是开发一种改进的VAM(MVAM),它可以通过实际实施来规避现有问题并提供更高的灵敏度。首先,将传统VAM中使用的振动器替换为压电陶瓷换能器,以提高其实用性。此外,线性扫描正弦信号代替了正弦波,用于低频(LF)泵浦振动和高频(HF)探测波。换句话说,不需要结构条件的先验知识,这进一步拓宽了应用范围。随后,采用时间反转(TR)方法来克服传统VAM中的信号能量耗散和低信噪比(SNR)等问题。此外,噪声辅助多元经验模态分解(NA-MEMD)和多尺度多元样本熵(MMSE)被用于开发新的破坏指数(DI),以监测螺栓的早期松动。最后,通过将建议的基于MMSE的DI与传统VAM方法的非线性DI进行比较,进行了多次重复的实验,以验证该方法的准确性以及在实际操作中简化螺栓早期松动监测的能力。由Elsevier Ltd.发布

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