Diagnosis method of fault location and performance degradation degree of rolling bearing based on optimal ensemble EMD

In order to more effectively diagnose the rolling bearing fault position and different performance degradation degrees simultaneously, a fault diagnosis method is introduced to achieve feature extraction and intelligent classification for the vibration signals of rolling bearing under different conditions. In this method, the vibration signal in each condition is decomposed using ensemble empirical mode decomposition (EEMD), however, the result depends on two important parameters, i.e. the number of ensemble trials and the amplitude of the added white noise. So, a rule of adding white noise in EEMD is presented. Using the series of intrinsic mode functions (IMF) obtained with EMD method and combined with singular value decomposition (SVD), the singular values for different conditions are obtained, which form the feature vector matrix. The obtained feature vector matrix is then used as the input of the improved hyper-sphere multi-class support vector machine for classification. Thereby, the multi-status intelligent diagnosis of normal rolling bearings and the faulty rolling bearings at different fault locations and with different performance degradation degrees can be achieved simultaneously. Experiment results show that the presented rule of adding white noise in EEMD method can avoid artificially determining decomposition parameters and improve the decomposition efficiency. Compared with the diagnosis method based on EMD combined with autoregressive (AR) model, the intelligent diagnosis method based on optimal parameter EEMD combined with SVD has higher recognition rate.