Bolted connection functions to fasten and secure parts together for engineering structures. Newly developed percussion-based approaches have been proven as a fast and effective tool for bolt looseness identification; however, most of the existing studies use machine learning assisted approaches to classify percussion sounds and predict looseness conditions without investigating the relationship between percussion sounds and bolt vibrations. This paper presents a conceptual research to utilize bolt vibration signal to reconstruct percussion sounds, which significantly improves the validity and accuracy of bolt looseness identification. In the experimental study, a laser Doppler vibrometry was used to capture vibrational information of the test bolt; meanwhile, percussion sounds were collected by microphones. The relationship between sound and vibration signals was investigated using wavelet packet decomposition and correlation analysis. A new set of sounds were reconstructed by combination of the sound packets which showed the strongest correlation with the vibration signal. The reconstructed sound database was then transformed into spectrograms and trained by a two-dimensional convolution neural network to identify bolt looseness conditions.

中文翻译：

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使用振动引导声音重建的基于敲击的螺栓松动识别

螺栓连接功能可将工程结构的零件固定在一起。新开发的基于冲击的方法已被证明是一种快速有效的螺栓松动识别工具；然而，现有的大多数研究都使用机器学习辅助方法来对敲击声进行分类并预测松动情况，而没有研究敲击声和螺栓振动之间的关系。本文提出了利用螺栓振动信号重构敲击声的概念研究，显着提高了螺栓松动识别的有效性和准确性。在实验研究中，使用激光多普勒测振仪来捕捉测试螺栓的振动信息；同时，敲击声被麦克风收集。使用小波包分解和相关分析研究了声音和振动信号之间的关系。通过组合与振动信号表现出最强相关性的声音包来重构一组新的声音。然后将重建的声音数据库转换为频谱图，并通过二维卷积神经网络进行训练，以识别螺栓松动情况。