Different from traditional health-monitoring methods based on vibrational signals recorded by contact sensors, an online diagnosis procedure for steel truss structures using sound signals was proposed. The basic idea of the procedure was to identify the features related to bolt connection damage extracted from sound signals and locate the damaged position. Before the online diagnosis was carried out, sound signals were specifically collected by a microphone array involving environmental noise and sound discharged by artificial damaged bolt connections. Then the signals were preprocessed and their time and frequency domain features were extracted, from which sensitive features were selected by support vector machine recursive feature elimination. A support vector machine classifier aiming to identify signals related to damage was trained with the selected sensitive features, and a genetic algorithm was used to optimize its parameters. An improved method called steered response power and phase transformation with offline database was put forward to compute the steered response power values of coordinates in the offline database to localize the source of identified damage signals. The pre-built database consisted of a series of coordinates indicating the positions of bolts. When the online diagnosis was implemented for a steel truss structure, sound signals were picked up by the microphone array at the same location as that used for the database construction. The signals were preprocessed and their sensitive features were extracted for damage identification by the trained support vector machine classifier. If some signals were judged to be related to bolt connection damage, steered response power and phase transformation with offline database was used to compute steered response power values, with which a fusion decision was made based on evidence theory to locate the damaged bolt connection. The experiment of a steel truss model with 24 bolt connections showed that the proposed procedure could locate the loose bolts precisely even under heavy noise effect, and had a smaller computational load compared with traditional steered response power and phase transformation.

不同于传统的基于接触式传感器记录的振动信号的健康监测方法，提出了一种基于声信号的钢桁架结构在线诊断程序。该程序的基本思想是识别与从声音信号中提取的螺栓连接损坏有关的特征，并找到损坏的位置。在进行在线诊断之前，声音信号是通过麦克风阵列专门收集的，该麦克风阵列涉及环境噪声和人为损坏的螺栓连接发出的声音。然后对信号进行预处理并提取其时域和频域特征，然后通过支持向量机递归特征消除从中选择敏感特征。使用选定的敏感特征训练了旨在识别与损伤相关的信号的支持向量机分类器，并使用遗传算法优化了其参数。提出了一种改进的离线数据库转向响应功率和相位变换方法，用于计算离线数据库坐标的转向响应功率值，以定位已识别损伤信号的来源。预先建立的数据库由一系列指示螺栓位置的坐标组成。当对钢桁架结构执行在线诊断时，麦克风阵列会在与数据库构建所使用的位置相同的位置拾取声音信号。信号经过预处理，并通过训练有素的支持向量机分类器提取其敏感特征以进行损伤识别。如果判断某些信号与螺栓连接损坏有关，则使用转向响应功率和离线数据库的相位转换来计算转向响应功率值，然后基于证据理论做出融合决策，以定位损坏的螺栓连接。对具有24个螺栓连接的钢桁架模型进行的实验表明，与传统的转向响应功率和相变相比，所提出的方法即使在重噪声的影响下也可以精确地定位松散的螺栓，并且计算量较小。利用离线数据库的转向响应功率和相位变换来计算转向响应功率值，并根据证据理论做出融合决策，以定位受损的螺栓连接。对具有24个螺栓连接的钢桁架模型进行的实验表明，与传统的转向响应功率和相变相比，所提出的方法即使在重噪声的影响下也可以精确地定位松散的螺栓，并且计算量较小。利用离线数据库的转向响应功率和相位变换来计算转向响应功率值，并根据证据理论做出融合决策，以定位受损的螺栓连接。对具有24个螺栓连接的钢桁架模型进行的实验表明，与传统的转向响应功率和相变相比，所提出的方法即使在重噪声的影响下也可以精确地定位松散的螺栓，并且计算量较小。