Abstract
In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.
Highlights
Xinyu Wang and Jie MaMechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China
Rolling bearing is one of the important parts of rotating machinery, which ensures the working accuracy of the shaft [1]
Early fault vibration signals often have nonlinear and nonstationary characteristics, the impact component of fault characteristics is easy to be submerged in strong background noise, and it is difficult to judge the fault type directly from time domain or frequency domain [2, 3]. erefore, the early fault diagnosis of rolling bearing has always been a research difficulty and hot spot
Summary
Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China. In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. The low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and the fault location is determined. E results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and the fault location is determined. e results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
