Abstract
Variational mode decomposition (VMD) has been applied in the field of rolling bearing fault diagnosis because of its good ability of frequency segmentation. Mode number K and quadratic penalty term α have a significant influence on the decomposition result of VMD. At present, the commonly used method is to determine these two parameters adaptively through intelligent optimization algorithm, namely, the parameter-adaptive VMD (PAVMD) method. The key of the PAVMD method is the setting of an objective function, and the traditional PAVMD method is prone to overdecomposition or underdecomposition. To solve these problems, an improved parameter-adaptive VMD (IPAVMD) method is proposed. A new objective function, the maximum average envelope kurtosis (MAEK), is proposed in this paper. The new objective function fully considers the equivalent filtering characteristics of VMD, and squared envelope kurtosis has good antinoise performance. In the optimization method, this paper uses an improved particle swarm optimization (PSO) algorithm. The MAEK and PSO can make sure the IPAVMD method reaches the best complete decomposition of the signal without an underdecomposition or overdecomposition problem. Through the analysis of simulation data and experimental data, the performance of the IPAVMD and the traditional PAVMD is compared. The comparison results show that the proposed IPAVMD has better performance and stronger robustness than the traditional method and is suitable for both single-fault and multiple-fault cases of rolling bearings. The research results have certain theoretical significance and application value for improving the fault diagnosis effect of rolling bearings.
Highlights
Rolling bearing is widely used in rotating machinery, which is one of the damaged parts [1]
The collected vibration signals are generally nonlinear and nonstationary, and the fault feature information contained in them is often submerged by strong background noise [2]
Erefore, constructing an appropriate objective function is the key to the parameter-adaptive Variational mode decomposition (VMD) method. e authors find that the ensemble kurtosis is still affected by cyclic stationary noise, and the parameter-adaptive VMD proposed by Miao et al [26] has the problem of overdecomposition or underdecomposition
Summary
Rolling bearing is widely used in rotating machinery, which is one of the damaged parts [1]. E authors find that the ensemble kurtosis is still affected by cyclic stationary noise, and the parameter-adaptive VMD proposed by Miao et al [26] has the problem of overdecomposition or underdecomposition. E optimized VMD is used to decompose the signal, and the component with the maximum ensemble kurtosis value is selected, which is considered to contain the bearing fault feature information. E maximum average envelope kurtosis (MAEK) is taken as the objective function, and the optimal parameter combination of VMD is obtained through PSO search, as shown in equation (10). E improved parameter-adaptive VMD method can decompose the cyclic stationary interference part, fault pulse part, and random pulse part at one time, and the problem of overdecomposition or underdecomposition does not occur . In order to demonstrate the correctness and generalization ability of the IPAVMD method, a series of bearing signals, including single fault and compound fault, are used for simulation and experimental verification. e performance of the IPAVMD method and PAVMD method is compared. e comparison results show that IPAVMD can realize the complete decomposition of the signal and does not show the underdecomposition or overdecomposition phenomenon
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
