Adaptive variational mode decomposition based on Archimedes optimization algorithm and its application to bearing fault diagnosis
Adaptive variational mode decomposition based on Archimedes optimization algorithm and its application to bearing fault diagnosis
复制标题
基于阿基米德优化算法的自适应变分模态分解及其在轴承故障诊断中的应用
DOI：
10.1016/j.measurement.2022.110798
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发表时间：
2022-02-01
期刊：
影响因子：
5.6
通讯作者：
Xie, Zhijie
中科院分区：
文献类型：
--
作者：
Wang, Junxia;Zhan, Changshu;Xie, Zhijie
变分模态分解（VMD）在旋转机械故障诊断中应用广泛。然而，其主要参数的选择往往基于经验，这会影响分解结果。为了克服这一缺陷，提出了一种利用阿基米德优化算法（AOA）的自适应VMD方法。首先，将目标函数的计算域设置为信号包络谱的幅值谱。其次，提出了一种相关波形指数（Cwi）来评估信号的复杂性。将所有本征模态函数（IMFs）的Cwi的最小平均值作为目标函数。最后，利用AOA搜索最优模态数和惩罚因子，以找到对故障特征敏感的IMFs。与其他改进的VMD方法相比，该方法在从模拟和实际案例中提取故障特征方面具有更好的性能。
Variational mode decomposition (VMD) is widely used in rotating machinery fault diagnosis. However, the choice of its main parameters is often based on experience, affecting the decomposition results. Aiming to mitigate this drawback, an adaptive VMD method using the Archimedes optimization algorithm (AOA) is presented. Firstly, the computational domain of the objective function is set to the amplitude spectrum of the signal envelope spectrum. Secondly, a correlation waveform index (Cwi) is proposed to evaluate the complexity of the signal. The minimum average value of the Cwi of all intrinsic modal functions (IMFs) is taken as the objective function. Finally, the AOA is used to search for the optimal mode number and penalty factor to find IMFs which are sensitive to fault features. Compared to the other improved VMD methods, the proposed method has a better performance in extracting the fault characteristics from the simulated and actual cases.