振动
带宽(计算)
计算机科学
解算器
控制理论(社会学)
遗传算法
信号(编程语言)
信号处理
正常模式
算法
人工智能
声学
数字信号处理
物理
电信
机器学习
程序设计语言
控制(管理)
计算机硬件
作者
Aina Wang,Pan Qin,Xi‐Ming Sun,Yingshun Li
标识
DOI:10.1109/tii.2023.3285030
摘要
Variational mode decomposition (VMD) provides a feasible approach to decompose vibration signals obtained from complex machinery for further applications. The mode frequency bandwidth control parameter and the total number of modes are critical parameters for VMD. Thus, optimally and automatically setting the two parameters is an essential issue for VMD for various practical vibration signal sources. To this end, this work proposes an automatic parameter setting VMD for vibration signals. First, we use the bandwidth evaluation criterion and the mean mode-location distance to evaluate the sparsity of modes; we use the energy loss to evaluate the reconstruction from modes. Then, we synthesize the three criteria into a novel optimization model using logarithmic transformation. Accordingly, a genetic algorithm-based solver is developed for the optimization model. Finally, the artificial multi-component signal and the practical rolling bearing vibration signal from CWRU Laboratory are used to verify the performance of the proposed optimization method.
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