基于改进DBN的回转支承寿命状态识别

In order to solve the problems of large slewing bearing, such as large background noise, weak characteristic signal, and difficulty to identify the life state of slewing bearing, an identification method of slewing bearing life state based on improved deep belief network (DBN) was proposed. DBN network has strong deep learning ability and can effectively mine the running state information of slewing bearing, which solves the problem that traditional shallow network relies too much on the effect of feature extraction and low recognition accuracy. In DBN learning and training, a new optimized learning method,Free Energy in Persistent Contrastive Divergence(FEPCD), was proposed to solve the problem of the decline of approximation and classification ability of DBN in long-term learning. Then the superiority of the proposed method was verified by using the test data of test rig. Finally, the recognition results of the improved DBN algorithm and the shallow classification algorithm were compared. The results show that the improved DBN network can reflect the life characteristics of slewing bearing more accurately than the original DBN network and shallow algorithm, and the proposed method has the characteristics of stability and intelligence.