基于变分模态分解的机械故障诊断方法研究
发布时间：2018-07-16 18:09
【摘要】：变分模态分解(VMD)是一种新的自适应信号分解方法,该方法的核心思想是假设每个模态的绝大部分都是紧紧围绕在某一中心频率周围的,然后将模态带宽的求解问题转化为约束优化问题,求解出每个模态。将VMD引入到机械故障诊断中,提出一种基于VMD的机械故障诊断方法,并与集合经验模态分解(EEMD)方法进行对比分析。仿真结果表明:VMD方法明显优于EEMD方法,能有效地分解出信号的固有模态;与EEMD方法相比较,该方法模态混叠现象弱,计算效率高,理论充分。将VMD方法成功地应用到转子不同碰摩严重程度的故障数据分析实验中,实验结果进一步验证了该方法的有效性,能够揭示出碰摩故障数据的频率结构,区分碰摩故障的严重程度。
[Abstract]:Variational mode decomposition (VMD) is a new adaptive signal decomposition method. The core idea of this method is to assume that most of each mode is surrounded by a central frequency. Then, the problem of modal bandwidth is transformed into a constrained optimization problem, and each modal is solved. This paper introduces VMD into mechanical fault diagnosis, proposes a VMD-based mechanical fault diagnosis method, and compares it with the set empirical mode decomposition (EEMD) method. The simulation results show that the VMD method is obviously superior to the EEMD method and can effectively decompose the inherent modes of the signal. Compared with the EEMD method, the proposed method has the advantages of weak aliasing, high computational efficiency and sufficient theory. The VMD method is successfully applied to the fault data analysis experiment of rotor with different rubbing severity. The experimental results further verify the effectiveness of the method and reveal the frequency structure of the rub-impact fault data. Distinguish the severity of rubbing faults.
【作者单位】： 南昌航空大学无损检测技术教育部重点实验室;
【基金】：国家自然科学基金项目(51675258、51265039、51075372、50775208) 机械传动国家重点实验室开放基金项目(SKLMTKFKT-201514) 广东省数字信号与图像处理技术重点实验室基金项目(2014GDDSIPL-01) 国家重点研发计划项目(2016YFF0203000)
【分类号】：TH17
[Abstract]:Variational mode decomposition (VMD) is a new adaptive signal decomposition method. The core idea of this method is to assume that most of each mode is surrounded by a central frequency. Then, the problem of modal bandwidth is transformed into a constrained optimization problem, and each modal is solved. This paper introduces VMD into mechanical fault diagnosis, proposes a VMD-based mechanical fault diagnosis method, and compares it with the set empirical mode decomposition (EEMD) method. The simulation results show that the VMD method is obviously superior to the EEMD method and can effectively decompose the inherent modes of the signal. Compared with the EEMD method, the proposed method has the advantages of weak aliasing, high computational efficiency and sufficient theory. The VMD method is successfully applied to the fault data analysis experiment of rotor with different rubbing severity. The experimental results further verify the effectiveness of the method and reveal the frequency structure of the rub-impact fault data. Distinguish the severity of rubbing faults.
【作者单位】： 南昌航空大学无损检测技术教育部重点实验室;
【基金】：国家自然科学基金项目(51675258、51265039、51075372、50775208) 机械传动国家重点实验室开放基金项目(SKLMTKFKT-201514) 广东省数字信号与图像处理技术重点实验室基金项目(2014GDDSIPL-01) 国家重点研发计划项目(2016YFF0203000)
【分类号】：TH17
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