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桥梁在复杂恶劣的服役环境中运行，其健康监测系统常受各种环境因素的影响，导致系统中存在噪声。噪声会显著降低桥梁结构健康监测数据的准确性，因此，对桥梁健康监测信号进行去噪处理至关重要。提出了一种选择小波去噪参数的方法。引入稀疏指数(SI)作为量化评价指标，用于选择最佳的小波基；利用统计过程控制图理论的迭代算法，确定阈值。仿真结果表明，该方法相比其他方法具有更好的去噪效果，并能更有效地处理桥梁结构健康监测数据。
Abstract：The operation of bridges in complex and harsh service environments is often influenced by various environmental factors, leading to the presence of noise in their health monitoring systems. Noise can significantly reduce the accuracy of bridge structural health monitoring data. Therefore, it is crucial to denoise the bridge health monitoring signals. This study proposes a method for selecting wavelet denoising parameters. The sparse index(SI) is introduced and used as a quantification evaluation metric to select the optimal wavelet basis. A threshold is determined by using an iterative algorithm based on the theory of statistical process control charts. Simulation results demonstrate that compared with other methods, this method provides better denoising effects and can process bridge structural health monitoring data more effectively.
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基本信息:
DOI：
中图分类号:TN911.4;U446
引用信息:
[1]张云龙,夏云霞,闰金明等.基于小波阈值算法的桥梁结构健康监测信号去噪研究[J].青岛理工大学学报,2025,46(03):9-16+71.
基金信息:
山东省自然科学基金(ZR2023ME105)