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Comparative study between VMD and EMD in bearing fault diagnosis

This paper proposes a novel Variational mode decomposition (VMD) algorithm for bearing fault diagnosis. The Fast Fourier Transform fails to analyse the transient and non-stationary signals. Discrete Fourier transform and Empirical mode decomposition do not have the ability to attain the accurate Intrinsic mode functions under dynamic system fault conditions because the characteristic of exponentially decaying dc offset is not consistent. EMD is a fully data-driven, not model-based, adaptive filtering procedure for extracting signal components. The EMD technique has high computational complexity and requires a large data series. The proposed technique has high accuracy and convergent speed, and is greatly appropriate for bearing fault diagnosis. This paper illustrates that VMD removes the exponentially decaying dc offset and evaluates its performance compared to EMD.