As one of the most common coupling elements in infrastructures, bolted joints play an important part in ensuring the integrity and safety of the whole system, whose failure may cause disastrous consequences. In recent years, precise detection and evaluation of bolt looseness have attracted numerous researchers' interest. However, the reliability of existing methods cannot be well guaranteed in long-term field detection, and real-time feedback is rather costly. This paper proposes a novel bolt looseness detection method based on audio recognition and deep learning. Firstly, a percussion experiment was designed to collect audio signals of bolts at different torque levels. Then, the time-domain bolt percussion signals were converted into Mel-frequency spectrograms, and the convolutional neural network (CNN) was adopted to mining deep information from the images for classification. To further verify the effect of different initial prestress levels on the vibration frequency of the bolted joint, a numerical study was conducted with the consideration of three different prestress levels. The results reveal that the proposed method has a high recognition accuracy in identifying bolt looseness conditions. Additionally, an iOS APP of acoustic vibration was established for real application. The prerecorded and untrained percussion audio was used to simulate the real-time bolt looseness detection, which shows its potential in real future applications.

中文翻译：

---

使用基于深度学习的预测模型自动检测结构螺栓松动

作为基础设施中最常见的耦合元件之一，螺栓连接在确保整个系统的完整性和安全性方面发挥着重要作用，其故障可能导致灾难性后果。近年来，螺栓松动的精确检测与评价引起了众多研究人员的兴趣。然而，现有方法的可靠性在长期现场检测中不能得到很好的保证，实时反馈的成本相当高。本文提出了一种基于音频识别和深度学习的螺栓松动检测方法。首先，设计了一个敲击实验来收集不同扭矩水平的螺栓的音频信号。然后，将时域螺栓敲击信号转换为梅尔频谱图，并采用卷积神经网络（CNN）从图像中挖掘深层信息进行分类。为了进一步验证不同初始预应力水平对螺栓连接振动频率的影响，在考虑三个不同预应力水平的情况下进行了数值研究。结果表明，该方法在识别螺栓松动情况时具有较高的识别精度。此外，还建立了一个声振动的iOS APP，供实际应用。预先录制和未经训练的打击乐音频用于模拟实时螺栓松动检测，这显示了其在未来实际应用中的潜力。在考虑三种不同预应力水平的情况下进行了数值研究。结果表明，该方法在识别螺栓松动情况时具有较高的识别精度。此外，还建立了一个声振动的iOS APP，供实际应用。预先录制和未经训练的打击乐音频用于模拟实时螺栓松动检测，这显示了其在未来实际应用中的潜力。在考虑三种不同预应力水平的情况下进行了数值研究。结果表明，该方法在识别螺栓松动情况时具有较高的识别精度。此外，还建立了一个声振动的iOS APP，供实际应用。预先录制和未经训练的打击乐音频用于模拟实时螺栓松动检测，这显示了其在未来实际应用中的潜力。