电子测量与仪器学报

施杰,伍星,刘韬.基于 MPDE-EEMD 及自适应共振解调的轴承故障特征提取方法[J].电子测量与仪器学报,2020,34(9):47-54

基于 MPDE-EEMD 及自适应共振解调的轴承故障特征提取方法

Method of bearing fault feature extraction based on MPDE-EEMD and adaptive resonance demodulation technique

DOI：

中文关键词: 故障诊断滚动轴承多种群差分进化集合经验模式分解自适应共振解调

英文关键词:fault diagnosis rolling bearing multiple population differential evolution ensemble empirical mode decomposition adaptive resonance demodulation technique

基金项目:国家自然科学基金面上项目(51875272)、云南省应用基础研究计划重点项目(201601PE00008)、云南农业大学自然科学青年基金(2015ZR13)、云南省教育厅科学研究基金(2019J0175)资助项目

作者	单位
施杰	1. 昆明理工大学机电工程学院，2. 云南农业大学机电工程学院
伍星	1. 昆明理工大学机电工程学院，2. 云南农业大学机电工程学院
刘韬	1. 昆明理工大学机电工程学院，2. 云南农业大学机电工程学院

Author	Institution
Shi Jie	1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology,2. Faculty of Mechanical and Electrical Engineering, Yunnan Agriculture University
Wu Xing	1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology,2. Faculty of Mechanical and Electrical Engineering, Yunnan Agriculture University
Liu Tao	1. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology,2. Faculty of Mechanical and Electrical Engineering, Yunnan Agriculture University

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中文摘要:

针对滚动轴承振动信号具有非线性、非平稳性和非高斯性,并且故障特征往往淹没于系统噪声之中而难于识别的问题, 提出了以多种群差分进化(multiple population differential evolution, MPDE) 算法来改进集合经验模式分解( ensemble empirical mode decomposition, EEMD) 的 MPDE-EEMD 消噪方法,并与自适应共振解调技术( adaptive resonance demodulation technique, ARDT)相结合实现故障特征提取。首先,为了解决 EEMD 中加入参数依靠人工选择且难以准确获取的问题,建立极值点分布特性评价函数,利用 MPDE 来寻优获取最佳白噪声幅值,实现 EEMD 自适应分解。然后,采用峭度与相关性相结合的准则对分解后的 IMF 分量进行自动筛选,将满足条件的有效信号进行重构,实现对原始振动信号的降噪处理。最后,采用 ARDT 自动确定对消噪信号进行带通滤波的带宽和中心频率,再通过包络解调提取出滤波信号的特征频率。将轴承仿真故障信号与实际故障信号用于算法的验证,结果表明 MPDE-EEMD+ARDT 能有效提取出轴承故障特征。

英文摘要:

According to the problems that the fault features identification of rolling bearing vibration signal, a method for fault feature extraction was proposed base on the improved EEMD with multiple population differential evolution (MPDE) and adaptive resonance demodulation technique (ARDT). Firstly, in order to solve the problem that the EEMD􀆳s parameters selection depending on individuals’ experiences, an evaluation function for distribution characteristics of extreme value points was established. It was used to optimize white noise amplitude using MPDE. Then, EEMD adaptive decomposition was implemented. Secondly, effective signals of the decomposed IMF components were reconstructed using criteria for kurtosis and relativity. The signal de-noising process was realized. Finally, the center frequency and bandwidth of band-pass filter was adaptively determined based on ARDT, and the fault characteristic frequency was extracted using envelop demodulation analysis. A simulation signals and a rolling bearing test results show the validity of the proposed method.

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