Research on Fault Diagnosis Method of Rolling Bearing Based on Improved Convolutional Neural Network

When a rolling bearing fails, the vibration signal of the bearing is unstable and the signal presents non-linear characteristics. As a result, the existing rolling bearing fault diagnosis system has a weak ability to extract the original signal, and the poor ability to identify the rolling bearing signal leads to the final diagnosis effect and expected performance. There is a big gap, in order to enhance the intelligence of the fault diagnosis system, improve the accuracy and generalization ability of the system, and adapt to the needs of factory big data fault diagnosis. This paper proposes a fault diagnosis method of rolling bearing based on improved convolution neural network. First, this method improves the existing activation function and pooling method. After the convolutional layer and pooling, a layer of convolutional layer is added, and the stochastic gradient descent algorithm is used to accelerate the training speed. At the same time, an improved uniformity is proposed. The variance is used as the loss function of the network. The method proposed in this paper is experimentally verified under the bearing data set of Case Western Reserve University, the classic rolling bearing data set, and the conclusion is drawn through the experiment: the experiment under the bearing data set of Case Western Reserve University of the classic rolling bearing data set has achieved better results than the traditional The model has better experimental results, good anti-dryness and better generalization ability. This diagnosis method provides a new idea for fault diagnosis methods, and has a good technical application prospect in industrial production.