Abstract
The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.
Highlights
The motor is a widely used rotating mechanical equipment
The empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method were used, and when k = 4, the intrinsic modal functions (IMFs) components by the MTCFVMD method for the motor bearing rolling element fault data were obtained and the envelope signal of each IMF component was obtained by the Hilbert transform
The fault diagnosis method based on the variational mode decomposition (VMD) method with the center frequency method of the double threshold is used for the bearing inner and outer rings of a motor, this method contains difficulties when applying it to the rolling element of motor bearings
Summary
The motor is a widely used rotating mechanical equipment. The rolling bearing is the most important component in the motor. Due to the long-term repeated action of the contact stress with the working surface of the rolling bearing, bearing fatigue, cracks, indentation, and other faults can occur, which causes abnormal vibration of the motor, and results in abnormal operation of the motor, or even the whole machine can fail, causing a major accident [9–11] It is of great scientific significance and application value to effectively analyze and accurately diagnose motor bearing faults. Compared with the recursive “screening” modes of the EMD method and EEMD method, the VMD method transforms the signal into non-recursive and variational mode decomposition modes, which have a solid mathematical theoretical basis It shows better noise robustness, and can effectively separate two pure harmonic signals with similar frequencies. The rolling element vibration data of the motor bearing is used to test the effectiveness of the proposed methods
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