Rolling bearing fault feature extraction under variable conditions using hybrid order tracking and EEMD

To effectively extract rolling bearing fault feature under variable conditions, a hybrid method based on order tracking and EEMD is proposed in this paper. This method takes the advantages of order tracking, ensemble empirical mode decomposition and 1.5 dimension spectrum. Firstly, order tracking is used to transform the time domain non-stationary vibration signal to angular domain stationary signal. Secondly, ensemble empirical mode decomposition is performed to decompose the angular domain stationary signal into a series of IMFs, and select the IMF in which the largest vibration energy occurs as the characteristic IMF. Thirdly, 1.5 dimension spectrum is further employed to analyze the characteristic IMF, and extract the fault features from background noise. The proposed method is applied to analyze the experimental vibration signals, and the analysis results confirm the effectiveness of the proposed method under variable conditions.