【摘要】 螺栓连接因其高可靠性、便于安装与维护的特点，广泛应用于输电铁塔结构，由于长期受到静动态载荷的影响，容易引发螺栓连接松动，造成结构承载能力下降，危及电力系统安全。针对上述问题提出了一种基于音频信号多特征信息的智能检测方法，选用输电铁塔中典型螺栓连接节点，通过定点锤击收集了不同预紧力状态的音频信号，基于小波包分解信号处理技术，获取了信号的能量值松动指标，并利用粒子群算法实现惩罚因子与核函数参数的优化，提出了融合音频信号多特征信息的支持向量机模型，实现了螺栓预紧力的定量识别。研究结果表明：粒子群优化支持向量机模型在分类准确率方面得到明显提高，最高准确率达到93.42%,相比优化前，模型识别准确率提高了9.2%,为输电铁塔中螺栓预紧力检测提供了有效方法。更多还原

【Abstract】 Bolt connections, characterized by high reliability and ease of installation and maintenance, are extensively employed in transmission tower structures. Due to the long-term influence of static and dynamic loads, bolt connections are prone to loosening, resulting in a decline in the structural bearing capacity and endangering the safety of the power system. In response to the abovementioned problems, an intelligent detection method based on multi-feature information of audio signals was proposed. A typical bolt connection node in the transmission tower was selected, and audio signals in different pre-tightening force states were collected through fixed-point hammering. Based on the wavelet packet decomposition signal processing technology, the energy value loosening index of the signals was obtained, and the optimization of the penalty factor and kernel function parameters was achieved using the particle swarm algorithm. A support vector machine model integrating multi-feature information of audio signals was proposed, realizing the quantitative identification of bolt pre-tightening force. The research results indicate that the particle swarm optimization support vector machine model has witnessed a notable improvement in classification accuracy, with the maximum accuracy reaching 93.42%. Compared with that before optimization, the model’s recognition accuracy has increased by 9.2%, providing an effective approach for the detection of bolt pre-tightening force in transmission towers.更多还原