Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network - Yu Xiao; Fan Chunyang; Dong Fei; Ding Enjie; Wu Shoupeng; Wang Xin (Jixie chuandong)
In order to realize the intelligent diagnosis of rolling bearing failure,a fault diagnosis model of vibration signal based on empirical mode decomposition（ EMD） and deep belief network（ DBN） is proposed.Firstly,the vibration signal is processed by empirical mode decomposition,and the statistical parameters of the effective intrinsic modal function（ IMF） component and its Hilbert envelope are obtained as the original feature set. Then,the extreme learning suite（ ELM） classifier feature selection method is proposed to remove the redundancy and interference characteristics of the original feature set and to select the fault state sensitive features. Finally,by using the depth learning advantages in high-dimensional and non-linear processing,the DBN-based fault feature adaptive analysis and fault state intelligent identification is completed. The results show that the ELM method can select the sensitive statistical characteristics of the fault,and the adaptive characteristic of the DBN method can effectively improve the accuracy of fault state recognition.