Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.

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As an important part of rotating machinery, bearings play an important role in large-scale mechanical equipment. Abnormal bearing conditions may cause the machine to malfunction, or even evolve into a serious accident. Therefore, the accurate and timely fault diagnosis of the bearing is of great significance. Based on EMD, this paper introduces the working principles and characteristics of EEMD and CEEMDAN, respectively. Then the signal was decomposed by EEMD and CEEMDAN respectively. The simulation results show that CEEMDAN has better effect on signal decomposition. Then, comparing the effect of CEEMDAN and EEMD on bearing fault feature frequency extraction, the experiment proves that CEEMDAN has a better ability to preserve original signal and eliminate noise than EEMD method, and can extract bearing fault feature more accurately and timely.

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