Abstract
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%.
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