Multi-fault diagnosis of rotating machinery via iterative multivariate variational mode decomposition

Abstract Multivariate variational mode decomposition (MVMD) is a novel extension of variational mode decomposition (VMD) for multi-channel data sets. It decomposes multi-component and multi-channel signals into multivariate modulated oscillations crossing different center frequencies and limited bandwidths with sparse characteristics. MVMD inherits all the limitations of VMD and faces challenges in processing mechanical failure signals. The pre-selected values of the mode number K and balance parameters α still have the most significant impact on the decomposition results. Although the parameter-optimization method solves the problem of parameter selection to a certain extent, the result is often not optimal, and it is difficult to deal with multi-fault signals. A new multi-fault diagnosis method is proposed in this paper to solve these problems. Firstly, a new index, called the weighted combined fault index, is proposed to evaluate the fault information contained in each mode decomposed by MVMD, which is the criterion for selecting the optimal mode. Secondly, an iterative decomposition algorithm based on MVMD is proposed to iteratively decompose different fault components into the optimal modes to extract all potential fault information. Benefiting from these algorithms, this method applies MVMD to multi-fault diagnosis with adaptive parameter selection. Through simulations and experiments, the effectiveness and superiority of the proposed method are verified.