An architecture of deep learning network based on ensemble empirical mode decomposition in precise identification of bearing vibration signal

This paper proposes a deep learning network (DLN) as the basis for a bearing fault diagnosis technique, which is constructed by the autoencoders and softmax classifier for the purpose of identifying the various multi-degree bearing fault. Firstly, the ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into intrinsic mode functions (IMFs). A high-dimensionality feature vector is formed by analyzing the statistical parameters in the time domain and the frequency domain of the first several IMFs. Then, this feature vector serves as the input for DLN to classify the bearing fault pattern. In a DLN, an autoencoder performs the unsupervised feature self-learning phase to generate a final significant feature vector for training the softmax classifier. Finally, the parameters of a complete DLN based on stacking auto-encoders and the softmax classifier together is fine-tuned with respect to supervised learning criterion aiming to optimize the classification error. Experimental results have shown a great effect for bearing fault diagnosis based on the proposed DLN. The identification accuracy result has been achieved in the bearing fault status even with the unpredictable defects tests on the inner race and roller element of bearing. Methodologies in this study offer confidence for complex data classification.