Successive variational nonstationary mode decomposition for bearing multi-fault diagnosis under time-varying speed conditions

Abstract

Rolling bearings are important components in mechanical machinery, and their failure will directly affect the normal operation of the machine. Therefore, the analysis of mechanical machinery vibration signals is crucial for ensuring the normal operation of the machinery. Successive variational mode decomposition (SVMD) is an important technique for decomposing a bearing stationary signal into its characteristic modes with a priori penalty factor. Therefore, it cannot handle nonstationary bearing signals. To tackle the above problems, a novel method, named successive variational nonstationary mode decomposition (SVNMD), is developed in this article. First, a new decomposition framework is proposed by adopting the constructed resampling operator to modify the optimization function of the SVMD, which eliminates the influence of frequency mixing. Second, in order to automatically determine the optimal penalty factor, an iterative selection scheme is developed, which is free from prior knowledge. Third, an instantaneous frequency estimation theory is proposed to obtain the common trend function of the signal. Finally, a time-frequency representation with high-energy concentration is obtained to accurately identify the fault characteristics of rolling bearings. Both the simulation and experimental verification have confirmed the productiveness of the SVNMD in diagnosing multiple faults of bearings under time-varying speed conditions.