Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, China.
School of Mechatronics Engineering, Foshan University, Foshan 528225, China.
Sensors (Basel). 2020 Mar 31;20(7):1946. doi: 10.3390/s20071946.
The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.
早期滚动轴承的振动信号是非平稳非线性的，故障信号很微弱，难以提取。针对这一问题，本文提出了一种遗传变异粒子群优化变分模态分解（GMPSO-VMD）算法，并将其应用于滚动轴承振动信号故障特征提取中。首先，采用最小包络熵作为 GMPSO 的目标函数，寻找 VMD 算法的最优参数组合。然后，采用优化后的 VMD 算法对滚动轴承振动信号进行分解，得到几个固有模态函数（IMF）。对 GMPSO-VMD 分解后的滚动轴承故障信号 IMF1 进行包络谱分析，准确提取出滚动轴承四种故障状态的特征频率。最后，利用 GMPSO-VMD 算法对模拟信号和滚动轴承故障振动信号进行分析。通过与固定参数变分模态分解（FP-VMD）算法、完全集合经验模态分解自适应噪声（CEEMDAN）算法和经验模态分解（EMD）算法进行比较，验证了 GMPSO-VMD 算法的有效性。
