Abstract
Variational mode decomposition (VMD) is a modern decomposition method used for many engineering monitoring and diagnosis recently, which replaced traditional empirical mode decomposition (EMD) method. However, the performance of VMD method specifically depends on the parameter that need to pre-determine for VMD method especially the mode number. This paper proposed a mode determination method using signal difference average (SDA) to determine the mode number for the VMD method by taking the advantages of similarities concept between sum of variational mode functions (VMFs) and the input signals. Online high-speed gear and bearing fault data were used to validate the performance of the proposed method. The diagnosis result using frequency spectrum has been compared with traditional EMD method and the proposed method has been proved to be able to provide an accurate number of mode for the VMD method effectively for rotating machinery applications.
Highlights
Signal decomposition method helps to reduce signal complexity and improve the efficiency of rotating machinery diagnosis
Some improvement methods have been developed for Empirical mode decomposition (EMD) such as ensemble EMD (EEMD), complementary EEMD (CEEMD), partial EEMD (PEEMD) and succinct and fast EMD (SF-EMD) which helps to solve mode mixing and end effect problem by taking advantages of noise addition, permutation entropy, sifting stop criterion and window width selection [5,6,7,8]
The method proposed in this paper overcomes the mode determination problem for Variational mode decomposition (VMD) method which provides an accurate number of mode for rotating machinery signals
Summary
Signal decomposition method helps to reduce signal complexity and improve the efficiency of rotating machinery diagnosis. A new signal decomposition method called variational mode decomposition (VMD) has been developed and proposed by Dragomiretskiy and Zosso which can surpasses EMD and EEMD method in rotating machinery diagnosis [12,13,14,15]. VMD helps to solve mode mixing problem in decomposition result by the shift from sifting process approach to alternating direction method of multipliers approach [16]. VMD method relies on three main concepts which are Wiener filtering, Hilbert transform and analytic signal, and frequency mixing and heterodyne demodulation It decomposes an input signal into its principal modes called variational mode functions (VMFs) that reproducing the input signal with different sparsity properties. Repeat the iterative process from 2 until the function is converge based on convergence criteria satisfied the condition of ∑ ‖u − u ‖ /‖u ‖ < ε , where ε is a given accuracy requirement
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