Abstract
Recently, variational mode decomposition (VMD) has attracted wide attention on mechanical vibration signal analysis. However, there are still some dilemmas in the application of VMD, such as the determination of the number of mode decomposition K and quadratic penalty term α. In order to acquire appropriate parameters of VMD, an improved parameter-adaptive VMD method based on grey wolf optimizer (GWO) is developed by taking the minimum average mutual information into consideration (GWOMI). Firstly, the parameters (K, α) are adaptively determined through GWOMI. Then, the vibration signal is decomposed by the developed method and effective modes are extracted according to the maximum kurtosis. Finally, the extracted modes are processed by Hilbert envelope analysis to acquire the incipient fault features. With the simulation and experimental analysis, it is clearly found that the developed method is effective and performs better than some existing ones.
Highlights
Rotating machinery has been extensively employed in the manufacturing, traffic and transportation, marine vessel, etc
After the signal is processed by the GWOMI-variational mode decomposition (VMD) method, the effective mode is extracted according to the maximum kurtosis
A parameter-adaptive VMD method based on GWOMI for fault diagnosis of rolling bearing is proposed in this work
Summary
Rotating machinery has been extensively employed in the manufacturing, traffic and transportation, marine vessel, etc. They are nonstationary, nonlinear, and covered by strong background noise, which makes it almost impossible to extract the incipient fault features from the raw signals [5, 6] To address these problems, many methods have been developed based on signal processing. Is method can decompose signals into some intrinsic mode functions, which have limited bandwidths and different center frequencies It can overcome the shortcoming of mode aliasing and has been widely used in the fault diagnosis of rotating machinery. Zhang et al utilized the grasshopper optimization algorithm to improve the parameter adaptiveness of VMD and this method was used to extract fault features successfully [24]. Gu et al reported an adaptive VMD based on GWO In this method, the minimum average envelope entropy was used as the objective function [3].
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