It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring.

对于洪水排放结构振动安全评估而言，至关重要的是过滤低频噪声，分离密集频率分量并从振动信号中准确获取高频分量。变分模式分解是一种新颖的信号自适应分解方法，可以有效地处理洪水排放结构。然而，变分模分解中的模数和二次罚项的不确定性直接影响振动信号的分解。因此，本研究提出了一种改进的变分模分解方法，用于振动信号处理。所提出的方法基于奇异熵和频率稳定性自适应地确定模数，以将结构振动分量（包括密集频率分量和高频分量）和噪声分量从振动信号中完全分离出来。接下来，提出了一种基于样本熵和互信息的客观二次罚项函数，以量化结构振动分量之间的模态混合。最后，提出了一种基于甲虫天线搜索的粒子群算法对二次惩罚项进行优化，克服了传统算法的缺点，抑制了结构振动分量之间的模态混合。仿真信号验证了该方法的有效性和可行性，并将其应用于水闸样机工程。结果表明，该方法有效地过滤了噪声，大大提高了振动响应信噪比，获得了结构振动分量的时程信号，为泄洪结构振动安全性评估和健康监测提供了基础。