基于改进EMD的滚动轴承故障增长特征提取和损伤评估技术

raditional damage assessment methods for rolling bearings didnt consider sensitivity and stability of fault features to track fault growth process, it resulted in low precision and accuracy. This paper proposed a new feature extraction and damage assessment method for rolling bearings based on improved empirical mode decomposition (EMD) and singularity value decomposition (SVD) . For the different damage severity of each fault, it decomposed original vibration signals into a finite number of intrinsic mode functions (IMF) , and calculated their singular value sequences, thus built SVD components related to fault growth trends. Through analyzing the sensitivity and stability for fault growth trends described by SVD components, the optimal SVD sequences which could track fault growth process were selected as faulty eigenvectors of fault type recognition and damage assessment. Finally, it verified the effectiveness of proposed methods by using the open vibration data of rolling bearing provided by case western reserve university. The results shows that the fault growth features can be selected from complex SVD components by comparing their abilities of monitoring and tracking fault growth process, and the proposed approach can provide a better strategy for fault feature extraction of rolling bearings in order to improve damage assessment precision and accuracy.