摘要
综合考虑了轴承故障包络谱特征频率与倍频成分周期特征,建立了一种可以消除偶然冲击载荷影响与强背景噪声周期冲击特征的判断指标,对于RSSD方法需要通过人工方法设置品质因子的需求,建立标准ESMK作适应度函数,再根据包络谱诊断轴承运行过程的故障信号。外圈故障信号结果表明:受到偶然性冲击时形成了比轴承故障冲击更大的幅度,此时已经不能获取明确的轴承外圈故障冲击特征。形成了176 Hz与263 Hz的倍频数据,表明轴承中已经存在外圈故障。内圈故障信号结果表明:受噪声因素的强烈干扰后,已观察不到明显的内圈故障特征频率,冲击成分也获得了显著强化。形成了261.1 Hz与391.2 Hz的明显倍频,表明轴承中已经存在内圈故障。该研究能够有效提取获得轴承振动信号特征,也能适用于其它的机械传动领域。
Considering the characteristic frequency of bearing fault envelope spectrum and the periodic characteristics of frequency doubling components comprehensively,a judgment index that can eliminate the influence of accidental impact load and the periodic impact characteristics of strong background noise is established.For the requirement of RSSD method which needs to set the quality factor by manual method,standard ESMK is established as the fitness function.Then the fault signal of the bearing running process is diagnosed according to the envelope spectrum.The results show that the impact amplitude of the bearing fault is larger than that of the bearing fault when it is subjected to accidental impact,and the clear impact characteristics of the bearing outer ring fault cannot be obtained at this time.The double frequency data of 176 Hz and 263 Hz are formed,indicating that there is an outer ring fault in the bearing.The results of the inner ring fault signal show that no obvious inner ring fault characteristic frequency can be observed after the strong interference of noise factor,and the impact component has been significantly enhanced.An obvious frequency doubling of 261.1 Hz and 391.2 Hz is formed,indicating that there is an inner ring fault in the bearing.This research can effectively extract the bearing vibration signal characteristics,and can also be applied to other mechanical transmission fields.
作者
李有新
袁有栋
林占宏
LI Youxin;YUAN Youdong;LIN Zhanhong(Department of Mechanical and Electrical Engineering,Qinghai Higher Vocational and Technical Institute,Haidong Qinghai 810799,China;School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Qinghai Salt Lake TRIMAG Co.,Ltd.,Xining 810000,China)
出处
《机械设计与研究》
CSCD
北大核心
2024年第2期139-142,共4页
Machine Design And Research
基金
青海省级科技研究项目(2020-GX-C06)。
关键词
故障诊断
轴承
特征提取
稀疏分解
fault diagnosis
bearing
feature extraction
sparse decomposition
作者简介
李有新(1981-),男,副教授,主要从事于机械产品设计与开发,已发表论文8篇,E-mail:qh_liyouxin@163.com。
