Research on gearbox temperature field image fault diagnosis method based on transfer learning and deep belief network
Xi Lu, Pan Li
Abstract
This paper applies thermal imaging technology to gearbox fault diagnosis. The temperature field calculation model is established to obtain the temperature field images of various faults. A deep learning network model combining transfer learning of convolutional neural network with supervised training and unsupervised training of deep belief network is proposed. The model requires one-fifth of the training time of the convolutional neural network model. The data set used for training the deep learning network model is expanded by using the temperature field simulation image of the gearbox. The results show that the network model has over 97% accuracy for the diagnosis of simulation faults. The finite element model of gearbox can be modified with experimental data to obtain more accurate thermal images, and this method can be better used in practice.