Fault Diagnosis of Rolling Bearing Based on Improved LeNet-5 CNN

To solve the problem of fault diagnosis of rolling bearing caused by large amount of data and difficulties of processing those data on to bearing set, based on Convolution Neural Network, a new method of data processing is proposed in this paper. With this method, one-dimensional time domain signal can be transformed into two-dimensional images, which is more suitable for Convolutional Neural Network processing. Meanwhile, the traditional machine learning method has the disadvantage of low robustness and low recognition rate with noise interference. Therefore, based on the feature extraction of Convolution Neural Network, in this paper we proposed an improved LeNet-5 Convolution Neural Network model, that is, adding a convolution layer and a pooling layer to the classic LeNet-5 model. The hidden layer features are extracted by using the trainable convolution kernel, while the extracted implicit features are reduced by the pooling layer, the Softmax classifier is used for classification and recognition of rolling bearing faults. In this paper we verified the effectiveness of the improved LeNet-5 model for fault diagnosis of rolling bearing by using the rolling bearing data to train the classic LeNet-5 model and the improved model.