An enhanced empirical Fourier decomposition method for bearing fault diagnosis

To address the problem that bearing fault signals are usually contaminated by strong background interference due to multiple structures and complex transmission paths, which affects accurate fault feature extraction, an enhanced empirical Fourier decomposition technique was proposed in this paper. First, in order to weaken the influence of transmission path, the trend-line-extraction-based method was utilized in advance, which suppressed the signal distortion and background noise interference. Then, to achieve the appropriate parameter for the empirical Fourier decomposition, the correlation-coefficient-based decomposition number selection approach was constructed to avoid the existence of irrelevant modal functions. The band improvement strategy was proposed to reduce the invalid frequency bands with too narrow bandwidth during the decomposition process, the weighted harmonics significant index was utilized as the target, and the optimal modal components were also determined. Last, the fast Fourier transform was employed, and the bearing fault signatures were accurately detected. The simulation and experimental bearing fault signals were used for analysis; with the help of some comparisons, the analyzed results show that this method can effectively extract the fault characteristics of rolling element bearing from strong background interference.

Automated summary

To address the problem of bearing fault signals being contaminated by strong background interference, an enhanced empirical Fourier decomposition technique was proposed. The method includes a trend-line-extraction-based method to weaken the influence of transmission path, a correlation-coefficient-based decomposition number selection approach to avoid irrelevant modal functions, and a band improvement strategy to reduce invalid frequency bands. The results show that this method effectively extracts fault characteristics from strong background interference.