Fault diagnosis for gearbox based on EMD-MOMEDA

In this paper, a new method for fault detection of parallel shaft gearbox based on the Empirical Mode Decomposition (EMD) and Multipoint Optimal Minimum Entropy Deconvolution (MOMEDA) is proposed. MOMEDA can overcome the shortcomings of minimum entropy deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD), and it is introduced to extract the fault cycle of gearbox signals. The vibration signals of gearbox are complex, including fault signals, noise signals and deterministic signals such as gear meshing component. Fault signal is often buried in these other components, which increases the difficulty of gearbox fault detection. Thus the EMD is proposed to decompose the signal and extract the fault impact components from the signal. The parallel shaft gearbox preset fault experiment is carried out to verify the effectiveness of method. In addition, some traditional methods, such as Fourier transform, cepstrum analysis, MED and MCKD, are used to compare with the proposed methods. Experimental results show that the effectiveness of the proposed method is better than that of traditional methods.