Optimization Application of Deep Belief Network in Gear Fault Diagnosis

Abstract

Deep belief networks (DBNs) in the process of the application of the gear vibration signal fault diagnosis, vector network depth, the number of neurons in hidden layer, set the initial parameters, such as there is a certain blindness, and for different data sets, the influence of initial parameters is different also, in order to get ideal fault recognition rate, the initial parameters have to be repeatedly debugging, This process is not only time-consuming and laborious, but also disadvantageous to its universal application in fault diagnosis. To solve this problem, this paper mainly optimizes the number of hidden layer neurons and learning rate of DBNs. The diagnosis example shows that after optimization, the initial parameter setting is more accurate and efficient, and the fault identification rate is significantly improved.