Based on the analysis of the bearing rotating speed feature and the vibration analysis technique, a novel fault diagnosis method of rotating bearing by adopting improved ensemble empirical mode decomposition (EEMD) and deep belief network (DBN) was proposed. Firstly, the EEMD method is adopted to decompose the collected vibration data into the combination of the several intrinsic mode functions (IMFs). Then the spectrum of IMF components and the spectrum of original data are compared to eliminate the false components and interference signals. Because the redundant extreme points or pseudo-extreme points seriously affect the construction of the mean curve of EEMD algorithm, three methods are put forward to eliminate the pseudo extreme points and the redundancy extreme points so as to improve the algorithm performance. Finally, the remaining IMF components are entered into the deep belief network (DBN) to extract the data features and realize the fault diagnosis. Simulation results on the rolling bearing data of the Bearing Data Center in Case Western Reserve University show the effective of the proposed method.

The rolling bearing fault diagnosis is affected by industrial environmental noise and other factors, leading to the existence of some redundant components after signal decomposition. At the same time, the existence of the modal aliasing phenomenon in empirical mode decomposition (EMD) and the relevant improved algorithms also leads to the existence of many invalid features in the components. These phenomena have great influence on the bearing fault diagnosis. So a rolling bearing bidirectional-long short term memory (Bi-LSTM) fault diagnosis method was proposed based on segmented interception auto regressive (SIAR) spectrum analysis and information fusion. The ensemble empirical mode decomposition (EEMD), the complementary ensemble empirical mode decomposition (CEEMD) and the robust EMD (REMD) algorithms decompose the rolling bearing fault signals, and AR spectrum analysis is performed on the obtained components respectively. By comparing the AR spectra of the components corresponding to different fault locations, the effective AR spectral values are intercepted as the eigenvalues of the data, and finally all the eigenvalues are fused to achieve the purpose of screening effective features more efficiently so as to reduce the impact of feature redundancy caused by mode aliasing on neural network training. Then the Bi-LSTM neural network was used as a rolling bearing fault diagnosis classifier, and the simulation experiments were conducted based on the rolling bearing fault signal data from Case Western Reserve University to verify the effectiveness of the proposed feature extraction and fault diagnosis method.