A novel automatic operational modal analysis method is proposed based on the image recognition of stabilization diagrams with uncertainty quantification. The method not only enriches the contents of the stabilization diagrams to make them much clearer—it can also avoid heavy manual analysis of the stabilization diagrams by automatically obtaining operational modal parameters. In order to increase the efficiency in identifying modal parameters of structures, a traditional stabilization diagram is re-constructed to convey the uncertainty estimates. These stabilization diagrams are then resolved into single mode stabilization diagrams (SMSDs) with a specified frequency interval, for image recognition. Subsequently, a convolutional neural network (CNN) is adopted to automatically analyze the SMSDs. In this study, the CNN is trained by the SMSDs derived from the stabilization diagrams of two numerical examples and three engineering structures. The trained CNN is then validated with a 6 degree-of-freedom model, the Heritage Court Tower building, and the Ting Kau Bridge. The robust learning and prediction results establish that the constructed CNN is effective for analyzing the stabilization diagrams of different structures. It can automatically and accurately identify the physical modes on the stabilization diagrams, without extracting any characteristic parameters.

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基于不确定性量化稳定图图像识别的结构自动运行模态分析

基于不确定性量化的稳定图图像识别，提出了一种新的自动操作模态分析方法。该方法不仅丰富了稳定图的内容，使其更加清晰，还可以通过自动获取运行模态参数，避免对稳定图进行繁重的人工分析。为了提高识别结构模态参数的效率，重新构建传统的稳定图来传达不确定性估计。然后将这些稳定图分解为具有指定频率间隔的单模稳定图 (SMSD)，用于图像识别。随后，采用卷积神经网络 (CNN) 来自动分析 SMSD。在这项研究中，CNN 由来自两个数值示例和三个工程结构的稳定图的 SMSD 训练。然后使用 6 自由度模型、Heritage Court Tower 大楼和汀九桥验证训练后的 CNN。稳健的学习和预测结果表明，构建的 CNN 可有效分析不同结构的稳定图。它可以自动准确地识别稳定图上的物理模式，而无需提取任何特征参数。稳健的学习和预测结果表明，构建的 CNN 可有效分析不同结构的稳定图。它可以自动准确地识别稳定图上的物理模式，而无需提取任何特征参数。稳健的学习和预测结果表明，构建的 CNN 可有效分析不同结构的稳定图。它可以自动准确地识别稳定图上的物理模式，而无需提取任何特征参数。