Abstract
A motor bearing system is a nonlinear dynamics system with nonlinear support stiffness. It is an asymmetry system, which plays an extremely important role in rotating machinery. In this paper, a center frequency method of double thresholds is proposed to improve the variational mode decomposition (VMD) method, then an adaptive VMD (called DTCFVMD) method is obtained to extract the fault feature. In the DTCFVMD method, a center frequency method of double thresholds is a symmetry method, which is used to determine the decomposed mode number of VMD according to the power spectrum of the signal. The proposed DTCFVMD method is used to decompose the nonlinear and non-stationary vibration signals of motor bearing in order to obtain a series of intrinsic mode functions (IMFs) under different scales. Then, the Hilbert transform is used to analyze the envelope of each mode component and calculate the power spectrum of each mode component. Finally, the power spectrum is used to extract the fault feature frequency for determining the fault type of the motor bearing. To test and verify the effectiveness of the DTCFVMD method, the actual fault vibration signal of the motor bearing is selected in here. The experimental results show that the center frequency method of double thresholds can effectively determine the mode number of the VMD method, and the proposed DTCFVMD method can accurately extract the clear time frequency characteristics of each mode component, and obtain the fault characteristics of characteristics; frequency, rotating frequency, and frequency doubling and so on.
Highlights
Time-frequency analysis method has been the most commonly applied method in the field of fault diagnosis of rotating machinery [1,2,3]
The analysis considers that the corresponding mode of the new frequency value the decomposed
The The power spectrum represents between signal power frequency; that is, power spectrum representsthe the relationship relationship between signal power andand frequency; that is, the distribution of the signal power in in thethe frequency transformisisused usedtotosolve solve the the distribution of the signal power frequencydomain
Summary
Time-frequency analysis method has been the most commonly applied method in the field of fault diagnosis of rotating machinery [1,2,3]. Vibration signals of rotating machinery usually contain a lot of information about the equipment health states. It is very significant to deeply analyze vibration signals for health monitoring and fault prediction of rotating machinery. The time-frequency analysis method is widely applied to process the vibration signals of rotating machinery in order to extract fault features by using signal decomposition and filtering [7,8,9,10,11,12,13,14,15,16]
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