Abstract

Bolted connections are prone to losing their preloads with the increasing service life, thus inducing engineering accidents and economic losses in industries. Therefore, it is important to detect bolt loosening, while current structural health monitoring methods mainly focus on single-bolt joints, whose applications in industries are limited. Thus, in this paper, a novel vibro-acoustic modulation (VAM) method, is developed to detect looseness of the multi-bolt connection. Compared to traditional VAM, the proposed method uses linear swept sine waves for both low-frequency and high-frequency excitations, which avoids a priori knowledge of the structure. Moreover, the orthogonal matching pursuit method is applied to compress original modulated signals and exclude redundant features. Then, a new entropy, namely the Gnome entropy with acronym gEn, is proposed in this paper. According to simulation analysis, the gEn has better anti-noise capacity and fewer parameters than traditional entropy. Finally, after quantifying the dynamic characteristics of compressed signals to obtain feature sets through the gEn, we feed feature sets into a random forest classifier and achieve looseness detection of the multi-bolt connection. Moreover, the proposed method in this paper has great potential to detect other structural damages and provides guidance for further investigations on the VAM method.

中文翻译：

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使用新型振动声学方法监控多螺栓连接的松动

摘要

随着使用寿命的延长，螺栓连接容易失去预紧力，从而导致工程事故和工业经济损失。因此，检测螺栓松动很重要，而当前的结构健康监测方法主要集中在单螺栓接头上，而该接头在行业中的应用受到限制。因此，在本文中，开发了一种新颖的振动声调制（VAM）方法来检测多螺栓连接的松动。与传统的VAM相比，该方法将线性扫描正弦波用于低频和高频激励，从而避免了对结构的先验知识。此外，正交匹配追踪方法被应用于压缩原始调制信号并排除冗余特征。然后，一个新的熵，即首字母缩写的Gnome熵提出了gEn。根据仿真分析，与传统的熵相比，gEn具有更好的抗噪能力和更少的参数。最后，在通过gEn量化压缩信号的动态特性以获得特征集之后，我们将特征集输入到随机森林分类器中，并实现了多螺栓连接的松动检测。此外，本文提出的方法在检测其他结构损伤方面具有很大的潜力，并为进一步研究VAM方法提供了指导。