Intelligent Diagnosis Method of Planetary Gear Based on Integrated Convolutional Neural Network
Aiming at the problems of planetary gearbox fault vibration characteristics that require preprocessing,identification difficulties,and slower convergence of the diagnostic model,an intelligent fault diagnosis method for planetary gearboxes based on integrated convolutional neural networks is proposed.First,one-dimensional convolution is used to extract features from the original time-domain vibration signal of the gear,and then two weak classifiers are used to update the sample weights according to the performance of the weak classification learning error rate,and the weak classifiers are trained according to the training set after adjusting the weights.Repeat this process,and finally integrate the weak classifiers by setting strategies to form an inte-grated convolutional neural network;establish a stable model for intelligent fault diagnosis of planetary gearboxes.Experimental results show that the integrated convolutional neural network can quickly diagnose the original vibration signals of planetary gears.Compared with the traditional convolutional neural network,it has stronger identification ability and faster convergence speed in the diagnosis of the original time domain vibration fault signal of the gear;the established intelligent diagnosis model can effectively diagnose the different fault states of the gear.
NETL
NSTL
万方数据
维普
