基于卷积神经网络DenseNet-CBAM的多螺栓结构冲击松动检测方法

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Chenfei Du, Jianhua Liu, Hao Gong, Jiayu Huang, Wentao Zhang
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引用次数: 0

摘要

螺纹紧固件广泛应用于机械系统中,具有连接、紧固、密封等功能。然而,在恶劣的环境中容易发生松动。松脱检测的重要性再怎么强调也不为过。基于冲击的松动检测方法因其方便、成本低而备受关注。然而,基于冲击法的多螺纹紧固件同时松动检测仍然是一个具有挑战性的问题,需要解决。本文提出了一种将冲击法与深度学习相结合的多螺栓松动检测方法。该方法包括三个集成模块,即信号预处理、松动信息增强和松动检测模块。第一个模块采用变分模态分解,将原始信号分解为一系列内禀模态函数,消除噪声的干扰。第二个模块采用压缩采样匹配追踪对去噪信号进行稀疏表示,并将稀疏信号与去噪信号融合,增强信号中的松动信息。最后,针对多分类任务,提出了结合注意机制的DenseNet-CBAM网络结构。实验结果表明,该方法在3种不同类型的带有螺纹紧固件的机械结构中检测精度均达到97%以上,具有较大的工程应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Percussion-based loosening detection method for multi-bolt structure using convolutional neural network DenseNet-CBAM
Threaded fasteners are widely applied in mechanical systems, providing the functions of connection, fastening, and sealing. However, loosening is vulnerable to occurring in harsh environment. The importance of loosening detection cannot be emphasized. Percussion-based loosening detection method has attracted much attention due to the convenience and low cost. However, the simultaneous loosening detection of multiple-threaded fasteners based on percussion method is still a challenging issue that needs to be addressed. This study proposes a novel multi-bolt loosening detection method combining percussion method, and deep learning. The method consists of three integrated modules, that is, signal preprocessing, loosening information enhancement, and loosening detection modules. In the first module, variational mode decomposition is used to decompose the original signal into a series of intrinsic mode function to eliminate the interference of noise. In the second module, compressive sampling matching pursuit is applied to represent the denoised signal sparsely, and the sparse signal is fused with the denoised signal to enhance loosening information in the signal. Last, DenseNet-CBAM network structure combining attention mechanism is proposed for multiple classification task. Experimental results showed that the proposed method achieved the detection accuracy of more than 97% in three different types of mechanical structures with multiple-threaded fasteners, indicating its great potentials in engineering applications.
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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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