高速轴承变工况故障数据AMO-VMD处理方法

AMO-VMD data processing method for high-speed bearing fault under variable working conditions

中文关键词  数据处理  模态分解  故障检测  高速轴承  相邻模态重叠指数

英文关键词  data processing  mode decomposition  fault detection  high-speed bearing  adjacent mode overlap index

中文摘要

      针对高速轴承故障信号噪声无法完全剔除导致变工况故障诊断精度较低的问题, 本文提出高速轴承变工况故障数据AMO-VMD处理方法, 并从多个维度验证所提算法对降噪性能的提升效果. 首先, 提出了降噪相邻模态重叠指数(AMO)确定变分模态分解(VMD)的最优分解层数, 可以充分发挥VMD频域分析的优势; 其次, 设计了基于包络谱熵最大原则的分量筛选降噪方法, 提高了故障信号频域信息表达准确度; 然后, 结合直流分量分离技术与绝对中位差(MAD)去异方法, 构建了针对高速轴承故障数据的数据清洗系统; 最后, 分别基于典型轴承故障仿真信号和高速试验台轴承故障信号验证所提算法的有效性, 结果表明, 所提方法较传统数据处理方法具有更高的故障信息甄别能力, 能够显著提高轴承变工况故障诊断的准确度.

英文摘要

      In response to the problem of low accuracy in variable condition fault diagnosis caused by the inability to completely eliminate the noise of high-speed bearing fault signals, this paper proposes an AMO-VMD processing method for variable condition fault data of high-speed bearings, and verifies the effectiveness of the proposed algorithm in improving noise reduction performance from multiple dimensions. First, the noise-reduction adjacent mode overlap (AMO) index is proposed to determine the optimal decomposition number of variational mode decomposition (VMD), which can fully leverage the advantages of VMD frequency domain analysis. Secondly, a component filtering and denoising method based on the principle of maximum envelope spectral entropy is designed to improve the accuracy of fault signal frequency domain information expression. Then, a data cleaning system for high-speed bearing fault data is constructed by combining DC component separation technology and median absolute deviation (MAD) de-differentiation method. Finally, the effectiveness of the proposed algorithm is verified based on typical bearing fault simulation signals and high-speed test rig bearing fault signals, respectively. The results show that the proposed method has higher fault information discrimination ability than traditional data processing methods, and can significantly improve the accuracy of bearing fault diagnosis under variable working conditions.