Author(s)

Jian Di, Leilei Wang

School of Control and Computer Engineering, North China Electric Power University, Baoding, China.

Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.

Fault Diagnosis, Rolling Bearing, Deep Auto-Encoder Network, CAPSO Algorithm, Feature Extraction

Di, J. and Wang, L. (2018) Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis. Journal of Computer and Communications, 6, 41-53. doi: 10.4236/jcc.2018.67005.