Feature extraction by enhanced analytical mode decomposition based on order statistics filter

The methods of mode decomposition based on the amplitude of spectrum are widely used in the fault diagnosis of bearings and gears in rotating machinery such as centrifuges, slurry pumps, fans, etc. Analytical mode decomposition with excellent filtering could set the bisecting frequency to distinguish useful modes. However, the vibration signal of equipment in actual operation is complex. This paper is devoted to the research of a novel spectral segmentation mode decomposition method. The proposed enhanced analytical mode decomposition takes advantage of the trend of spectrum fluctuations. In order to obtain the most critical bisecting frequency, the trend spectrum estimation method was proposed based on order statistics filter. The filtering effect of enhanced analytical mode decomposition was verified by the simulated signal. The experimental results show that the proposed method is efficient and the bearing inner and outer ring faults in the rotating machine can be successfully extracted.