摘要
针对滚动轴承故障冲击成分易淹没在强噪声中这一问题,提出了一种自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和粒子群优化的多点最优最小熵解卷积(partical swarm optimized multipoint optimal minimum entropy deconvolution adjusted,PSO-MOMEDA)相结合的故障诊断方法。由于MOMEDA的重要影响参数故障周期搜索范围T和滤波器长度L依赖人为经验选择,采用粒子群优化算法对这两个参数进行寻优。首先,采用CEEMDAN分解信号,依据峭度-相关系数准则筛选最优分量;其次,使用PSO对MOMEDA进行参数寻优,对最优分量使用MOMEDA进行滤波处理;最后,对滤波后的信号做包络谱分析,提取故障特征信息。通过仿真信号和实验信号分析表明,与单独使用CEEMDAN算法,将MOMEDA方法替换成MCKD方法相比较,该方法能够有效增强噪声中的故障冲击成分,准确提取轴承故障特征频率,准确诊断轴承故障。
Aiming at the problem that the impact components of rolling bearing fault are easily submerged in strong noise,a fault diagnosis method combining the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)optimized by partical swarm optimization algorithm(PSO)is proposed.Since MOMEDA's important influence parameters,the fault period search range T and the filter length L depend on human experience selection,particle swarm optimization algorithm is used to optimize these two parameters.Firstly,CEEMDAN was used to decompose the signal,and the optimal component was screened according to kurtosis-correlation coefficient criterion.Secondly,PSO is used to optimize the parameters of MOMEDA,and the optimal components are filtered by MOMEDA.Finally,the filter signal is analyzed by envelope spectrum to extract fault characteristic information.Simulation signal and experimental signal analysis show that this method can effectively enhance fault impact components and extract bearing fault characteristics.
作者
郭金泉
吴欣然
钟建华
GUO Jin-quan;WU Xin-ran;ZHONG Jian-hua(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;Fujian Key Laboratory of Force Measurement,Fuzhou 350100,China)
出处
《组合机床与自动化加工技术》
北大核心
2022年第10期164-168,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
福建省力值计量测试重点实验室(福建省计量科学研究院)开放课程基金资助(FJLZSYS202102)
2021天津大学-福州大学自主创新基金合作项目(TF2021-5)。
