Yu Yuanjing
School of Electrical Information Engineering, Yunnan Minzu University, Kunming, Yunnan 650500Yang Guangyong
School of Electrical Information Engineering, Yunnan Minzu University, Kunming, Yunnan 650500Yan Ting
School of Electrical Information Engineering, Yunnan Minzu University, Kunming, Yunnan 650500Xu Tianqi
School of Electrical Information Engineering, Yunnan Minzu University, Kunming, Yunnan 650500Ge Yihang
School of Electrical Information Engineering, Yunnan Minzu University, Kunming, Yunnan 650500School of Electrical Information Engineering, Yunnan Minzu University, Kunming, Yunnan 650500
TH133.33
When the rolling bearing fails, the fault characteristic signal will be mixed in the vibration signal, resulting in unsatisfactory extraction effect of the fault characteristic signal. To solve this problem, a fault extraction method for rolling bearings based on complementary integration of empirical mode decomposition and multi-point optimal minimum entropy (CEEMD-MOMEDA) is proposed. At first, the collected vibration signals are processed by CEEMD algorithm, and then the non-fault impact components are s[基金项目：国家自然科学基金项目（61761049,61261022）]creened out by kurtosis criterion. finally, the recombined signals are processed by MOMEDA algorithm to suppress the influence of noise and extract fault features. And compared with the single MOMEDA algorithm. The results show that the fault extraction ability and anti-interference ability of the proposed CEEMD-MOMEDA algorithm are greatly improved.
