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经验模态分解一类的递归算法所产生的模态混淆和端点效应将导致所获物理信息失真, 变分模态分解可改善这些问题. 但因其需预设参数, 对信号分解精度影响显著, 为此, 提出采用目标信号功率谱峰值所对应的频率以初始化变分模态分解所需中心频率, 借鉴经验模态分解递归模型, 基于能量截止法将变分模态分解改进为递归模式算法, 并采用粒子群优化算法对具有带宽约束能力的惩罚因子进行最优取值, 构成优化递归变分模态分解. 通过对比分析经验模态分解, 集成经验模态分解及优化递归变分模态分解在分解信号时的计算精度; 研究传统变分模态分解与优化递归变分模态分解在处理实际振动信号时计算速率. 结果表明: 优化递归变分模态分解在处理目标信号时精度最高, 与原分量相关性达99.9%; 与集成经验模态分解对比, 可由低至高将信号分解至不同频段, 物理意义更加清晰且不产生虚假模态; 处理实际非线性信号时, 优化递归变分模态分解无需预设分解模态个数, 相比于传统变分模态分解, 计算速率高12.5%—18.5%.Variational mode decomposition can improve traditional recursive algorithms, such as empirical mode decomposition, resulting modal aliasing and endpoint effects, but it has a significant influence on signal decomposition accuracy due to its pre-set parameters. The frequency corresponding to the peak value of the target signal power spectrum is proposed to initialize the center frequency required for the variational mode decomposition. The empirical mode decomposition and recursive model is used to improve the variational mode decomposition into the recursive mode algorithm based on the energy cutoff method. The group optimization algorithm optimally takes the penalty factor with bandwidth constraint ability to form an optimized recursive variational mode decomposition. By comparing with and analyzing empirical mode decomposition, integrating empirical mode decomposition and optimizing the computational accuracy of recursive variational mode decomposition in decomposing signals; studying traditional variational mode decomposition and optimizing recursive variational mode decomposition in dealing with actual vibration signals calculating rate, the results are obtained, showing that the optimized recursive variational mode decomposition has the highest accuracy when dealing with the target signal, and the correlation with the original component is 99.9%. Comparing with the integrated empirical mode decomposition, the signal can be decomposed into different frequency bands from low to high, and the physical meaning is clearer. No false modality is generated. When the actual nonlinear signal is processed, the optimized recursive variational mode decomposition does not need to preset the number of decomposition modes, and the calculation rate is 12.5%–18.5% higher than thay of the traditional variational mode decomposition.
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Keywords:
- variational mode decomposition/
- nonlinear/
- signal process
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编号 K,α 编号 K,α 1 12, 12100 14 12, 12400 2 12, 12900 15 12, 12000 3 12, 11800 16 12, 12000 4 12, 11900 17 13, 11900 5 12, 11800 18 12, 11900 6 12, 12700 19 12, 11400 7 12, 12100 20 12, 11900 8 12, 13100 21 13, 11900 9 12, 12100 22 12, 11900 10 12, 12000 23 12, 12000 11 11, 12000 24 12, 12000 12 11, 12000 25 11, 12100 13 12, 13200 — — 
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