Wang Baoxiang, Liu Guoqing, Dai Jihai, Ding Chuancang
School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
School of Rail Transportation, Intelligent Urban Rail Engineering Research Center of Jiangsu Province, Soochow University, Suzhou 215131, China.
Sensors (Basel). 2025 Jun 4;25(11):3542. doi: 10.3390/s25113542.
Accurate extraction of weak fault information from non-stationary vibration signals collected by vibration sensors is challenging due to severe noise and interference. While variational mode decomposition (VMD) has been effective in fault diagnosis, its reliance on predefined parameters, such as center frequencies and mode number, limits its adaptability and performance across different signal characteristics. To address these limitations, this paper proposes an improved variational mode decomposition (IVMD) method that enhances diagnostic performance by adaptively determining key parameters based on scale space representation. In concrete, the approach constructs a scale space by computing the inner product between the signal's Fourier spectrum and a Gaussian function, and then identifies both the mode number and initial center frequencies through peak detection, ensuring more accurate and stable decomposition. Moreover, a multipoint kurtosis (MKurt) criterion is further employed to identify fault-relevant components, which are then merged to suppress redundancy and enhance diagnostic clarity. Experimental validation on locomotive bearings with inner race faults and compound faults demonstrates that IVMD outperforms conventional VMD by effectively extracting fault features obscured by noise. The results confirm the robustness and adaptability of IVMD, making it a promising tool for fault diagnosis in complex industrial environments.
由于存在严重的噪声和干扰，从振动传感器采集的非平稳振动信号中准确提取微弱故障信息具有挑战性。虽然变分模态分解（VMD）在故障诊断中很有效，但其对中心频率和模态数等预定义参数的依赖，限制了其在不同信号特征下的适应性和性能。为解决这些局限性，本文提出了一种改进的变分模态分解（IVMD）方法，该方法通过基于尺度空间表示自适应确定关键参数来提高诊断性能。具体而言，该方法通过计算信号的傅里叶频谱与高斯函数之间的内积来构建尺度空间，然后通过峰值检测确定模态数和初始中心频率，确保更准确、稳定的分解。此外，进一步采用多点峭度（MKurt）准则来识别与故障相关的分量，然后将这些分量合并以抑制冗余并提高诊断清晰度。对具有内圈故障和复合故障的机车轴承进行的实验验证表明，IVMD通过有效提取被噪声掩盖的故障特征，优于传统的VMD。结果证实了IVMD的鲁棒性和适应性，使其成为复杂工业环境中故障诊断的一个有前途的工具。
