Guo Yuanjing, Yang Youdong, Jiang Shaofei, Jin Xiaohang, Wei Yanding
Zhijiang College, Zhejiang University of Technology, Shaoxing 312030, China.
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2022 May 20;22(10):3889. doi: 10.3390/s22103889.
Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.
滚动轴承是保证旋转机械正常运行的重要部件，由于运行条件恶劣，它也容易出现各种损坏。然而，通常很难从滚动轴承振动信号中提取微弱的故障特征信息并实现滚动轴承故障诊断。因此，本文提出了一种基于连续变分模态分解（SVMD）和能量集中度与位置精度（EP）指标的滚动轴承故障诊断方法。由于SVMD将滚动轴承的振动信号分解为多个模态，因此难以选择具有理想故障特征信息的目标模态。综合考虑故障特征分量的能量集中度和频率位置精度，提出了EP指标来指示目标模态。由于平衡参数对SVMD的性能至关重要且必须正确设置，因此引入了以EP指标为导向的线搜索方法来确定SVMD平衡参数的最优值。仿真和实验结果表明，所提出的SVMD方法对滚动轴承故障诊断是有效的，并且优于变分模态分解（VMD）方法。
