Cheng Liehai, Zhang Zhenli, Lacidogna Giuseppe, Wang Xiao, Jia Mutian, Liu Zhitao
Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan 250013, China.
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Torino, Italy.
Sensors (Basel). 2024 Oct 5;24(19):6447. doi: 10.3390/s24196447.
The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.
螺栓松动检测对于确保螺栓连接结构的完整性和安全性至关重要。基于敲击的螺栓松动检测提供了一种简单且经济高效的方法。然而，该方法存在一些固有缺点，限制了其应用。例如，它高度依赖检查人员的听力和经验，并且更容易受到环境噪声的影响。在本文中，提出了一整套信号处理程序，并构建了一种新型损伤指标向量，以增强该方法的可靠性和鲁棒性。首先，对原始音频信号执行一系列音频信号预处理算法，包括去噪、分割和平滑滤波。然后，利用累积能量熵（CEE）和梅尔频率倒谱系数（MFCC）来提取损伤指标向量，它们分别用作生成式和判别式分类器模型（高斯判别分析和支持向量机）的输入向量。最后，进行多次重复实验，以验证所提方法的有效性及其在音频信号方面检测螺栓松动的能力。在不同扭矩水平组合下，训练模型的测试准确率分别接近90%和96.7%。
