Li Nuo, Wang Hang
Key Subject Laboratory of Nuclear Safety and Simulation Technology, College of Nuclear Science and Technology, Harbin Engineering University, Harbin 150001, China.
Nuclear Power Institute of China, Chengdu 610213, China.
Entropy (Basel). 2025 Mar 7;27(3):277. doi: 10.3390/e27030277.
Variational Mode Decomposition (VMD) serves as an effective method for simultaneously decomposing signals into a series of narrowband components. However, its theoretical foundation, the classical Wiener filter, exhibits limited adaptability when applied to broadband signals. This paper proposes a novel Variable Filtered-Waveform Variational Mode Decomposition (VFW-VMD) method to address critical limitations in VMD, particularly in handling broadband and chirp signals. By incorporating fractional-order constraints and dynamically adjusting filter waveforms, the proposed algorithm effectively mitigates mode mixing and over-smoothing issues. The mathematical framework of VFW-VMD is formulated, and its decomposition performance is validated through simulations involving both synthetic and real-world signals. The results demonstrate that VFW-VMD exhibits superior adaptability in extracting broadband signals and effectively captures more rolling bearing fault features. This work advances signal processing techniques, enhancing capability and significantly improving the performance of practical bearing fault diagnostic applications.
变分模态分解（VMD）是一种将信号同时分解为一系列窄带分量的有效方法。然而，其理论基础——经典维纳滤波器，在应用于宽带信号时适应性有限。本文提出了一种新颖的可变滤波波形变分模态分解（VFW-VMD）方法，以解决VMD中的关键局限性，特别是在处理宽带信号和啁啾信号方面。通过纳入分数阶约束并动态调整滤波器波形, 该算法有效地减轻了模态混叠和过度平滑问题。构建了VFW-VMD的数学框架，并通过涉及合成信号和实际信号的仿真验证了其分解性能。结果表明，VFW-VMD在提取宽带信号方面具有卓越的适应性，并能有效捕捉更多滚动轴承故障特征。这项工作推动了信号处理技术的发展，增强了能力，并显著提高了实际轴承故障诊断应用的性能。
