Yan Xiaoan, Liu Tao, Fu Mengyuan, Ye Maoyou, Jia Minping
School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China.
School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
Sensors (Basel). 2022 Aug 18;22(16):6184. doi: 10.3390/s22166184.
Aimed at the problem of fault characteristic information bearing vibration signals being easily submerged in some background noise and harmonic interference, a new algorithm named enhanced differential product weighted morphological filtering (EDPWMF) is proposed for bearing fault feature extraction. In this method, an enhanced differential product weighted morphological operator (EDPWO) is first constructed by means of infusing the differential product operation and weighted operation into four basic combination morphological operators. Subsequently, aiming at the disadvantage of the parameter selection of the structuring element (SE) of EDPWO depending on artificial experience, an index named fault feature ratio (FFR) is employed to automatically determine the flat SE length of EDPWO and search for the optimal weighting correlation factors. The fault diagnosis results of simulation signals and experimental bearing fault signals show that the proposed method can effectively extract bearing fault feature information from raw bearing vibration signals containing noise interference. Moreover, the filtering result obtained by the proposed method is better than that of traditional morphological filtering methods (e.g., AVG, STH and EMDF) through comparative analysis. This study provides a reference value for the construction of advanced morphological analysis methods.
针对故障特征信息所承载的振动信号容易被某些背景噪声和谐波干扰淹没的问题，提出了一种名为增强差分积加权形态滤波（EDPWMF）的新算法用于轴承故障特征提取。在该方法中，首先通过将差分积运算和加权运算融入四个基本组合形态算子来构建增强差分积加权形态算子（EDPWO）。随后，针对EDPWO的结构元素（SE）参数选择依赖人工经验的缺点，采用故障特征比（FFR）指标自动确定EDPWO的扁平SE长度并搜索最优加权相关因子。仿真信号和实验轴承故障信号的故障诊断结果表明，所提方法能够从包含噪声干扰的原始轴承振动信号中有效提取轴承故障特征信息。此外，通过对比分析可知，所提方法获得的滤波结果优于传统形态滤波方法（如AVG、STH和EMDF）。本研究为先进形态分析方法的构建提供了参考价值。
