A parameter-adaptive variational mode decomposition approach based on weighted fuzzy-distribution entropy for noise source separation
Qidi Zhou, Junhong Zhang, Tianyi Zhou, Yongbo Qiu, Hongjie Jia, Jiewei Lin
Abstract
Abstract Due to limitations in the generalisation ability of currently proposed improved variational mode decomposition (VMD) methods, it is hard to precisely and efficiently discern signal characteristics from different power systems. Meanwhile, it is difficult to separate non-order noise sources in current studies. To address this issue, a novel scheme is proposed based on parameter-adaptive VMD and partial coherence analysis (PCA) for separating noise sources. In this approach, weighted fuzzy-distribution entropy (FuzzDistEn) is constructed to optimise the VMD to adaptively obtain the optimal parameters, considering the complexity of the signal system, and the mutual information between the decomposition components and the original signal. To verify the effectiveness and superiority of the proposed method, the paper respectively compares the decomposition results of the simulated signal using different objective functions, and shows that the weighted FuzzDistEn has a better decomposition effect. For the other issue, PCA is adapted to estimate the coherence between component vibration and radiated noise. In a case study, the parameter-adaptive VMD-PCA approach is implemented in a diesel engine noise identification field based on bench experiments. The results show that the proposed method can successfully separate five surface radiated noise sources. The research offers a new perspective on feature extraction problems.