基于小波包变换与CEEMDAN的滚动轴承故障诊断方法
Rolling bearing fault diagnosis method based on wavelet packet transform and CEEMDAN
栾孝驰 1李彦徵 1徐石 1沙云东1
作者信息
- 1. 沈阳航空航天大学航空发动机学院,沈阳 110136
- 折叠
摘要
针对滚动轴承诊断受环境噪声影响,特征频率难以提取的问题,提出了一种基于小波包变换与完全自适应噪声集合经验模态分解(CEEMDAN)的滚动轴承故障诊断方法.通过CEEMDAN将传感器收集到的原始振动信号进行分解并依据峭度值-相关系数(K-C)筛选准则划分高噪信号和低噪信号.利用小波包变换分解高噪信号后选取合适分量重构实现环境噪声的滤除并与低噪信号进行整合产生新的振动信号进行包络解调,提取实际故障特征频率实现滚动轴承的故障诊断.经对比试验,所提出的方法清晰地提取出滚动轴承的转频、故障特征频率及其倍频和调制频率,由仿真信号计算可知降噪后的信号信噪比提高了7.61 dB,有效优化了对噪声滤除的效果.
Abstract
For the problem that rolling bearing diagnosis is affected by the environmental noise so that extraction of characteristic frequency is difficult,a rolling bearing fault diagnosis method based on wavelet packet transform and complete ensemble empirical model decomposition adaptive noise(CEEMDAN)was proposed.The raw vibration signal collected by the sensor was split through CEEMDAN and the high-noise signal and low-noise signal were divided through the kurtosis-correlation coefficient screening criteria(K-C).The wavelet packet transform was used to split the high noise signal and then select appropriate component reconstruction to filter out the environmental noise and integrate with the low noise signal to generate a new vibration signal for envelope demodulation,and the actual fault characteristic frequency was extracted to achieve fault diagnosis of rolling bearings.After comparative experiments,the method proposed clearly extracted the rotational frequency,fault characteristic frequency and its frequency multiplier and modulation frequency of rolling bearings,and the signal-noise ratio after noise reduction was increased by 7.61 dB from the simulation signal calculation,which effectively optimized the effect of noise filtering.
关键词
完全自适应噪声集合经验模态分解(CEEMDAN)/峭度值-相关系数筛选准则/小波包变换/包络解调/特征频率/故障诊断Key words
complete ensemble empirical model decomposition adaptive noise(CEEMDAN)/kurtosis-correlation coefficient screening criteria/wavelet packet transform/envelope demodulation/characteristic frequency/fault diagnosis引用本文复制引用
基金项目
辽宁省教育厅系列项目(JYT2020010)
2022年辽宁省大学生创新创业训练计划(S202110143021)
中国航发产学研合作项目(HFZL2018CXY017)
出版年
2024
NETL
NSTL
万方数据