摘要
受环境噪声及信号衰减的影响,强背景噪声下的滚动轴承故障特征往往表现得非常微弱。滚动轴承的微弱故障特征提取一直是难点。稀疏分解在滚动轴承的故障特征提取中已经取得一定的应用。但其在强背景噪声干扰下滚动轴承微弱信号故障的特征提取效果并不明显。将最小熵解卷积(Minimum entropy deconvolution,MED)与稀疏分解相结合用于滚动轴承的微弱故障特征提取。用MED对强噪声滚动轴承信号进行降噪处理,对降噪后的信号进行稀疏分解和故障特征提取,取得了较好的效果。通过仿真和试验验证了所述方法的有效性及优点。
The rolling bearing is one of the key mechanical parts whose fault diagnosis is very important. The roiling bearing＇s fault feature under strong background noise is very weak for reasons of environment noise impact and signal attenuation. The feature extraction of rolling beating＇s weak fault is not only very important but also very hard. The sparse decomposition has been used in the fault feature extraction of rolling bearing. But its performance is very poor when the background noise is very strong. The minimum entropy deconvolution （MED） and sparse decomposition are combined for rolling bearing＇s weak fault diagnosis. The strong background noise of rolling bearing is decreased by the MED method firstly, then the de-noised signal is handled by the sparse decomposition. At last the envelope demodulation is carried on the last given signal and better result is obtained. In the end through simulation signal and experiment the effectiveness and advantage of the proposed method are verified.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2013年第1期88-94,共7页
Journal of Mechanical Engineering
基金
国家自然科学基金资助项目(51035007
51175239
5110543)
关键词
最小熵解卷积
稀疏分解
滚动轴承
微弱故障
特征提取
Minimum entropy deconvolution Sparse decomposition Roiling beatings Weak fault Feature extraction
