Improvement of one-dimensional convolution neural network and its application in gearbox fault diagnosis

Abstract

Gearbox is a key component of large rotating machinery. To ensure the safe operation of large rotating machinery and to meet the demand for automatic and intelligent gearbox fault diagnosis, this paper investigates the use of the frequency spectrum of gearbox vibration signals in an improved One-dimensional Convolution Neural Network (1D-CNN) to diagnose early weak faults in gearboxes. In view of the problem that the maximum pooling algorithm loses too much information and the average pooling algorithm blurs important information in the common convolution neural network (CNN), a small-scale convolution kernel with a moving step of 3 is proposed to replace pooling operation, so as to realize the role of automatically adjusting the weight extraction characteristics in the process of down-sampling and model training. Also, the global max pooling is used to extract the effective information of the final convolution layer instead of the global average pooling, and the regularized Dropout technique is used to further optimize the deep convolution neural network (DCNN), significantly reducing the number of network parameters and minimizing the phenomenon of network over-fitting. The research is carried out on an experimental platform for gearbox failures, the results show that the improved 1D-CNN has a correct fault identification rate of over 98%, which is 20% higher than that of the previous CNN, and the network has better noise immunity and stability. The method in this paper can provide a basis for researchers in the construction of DCNN and has certain practical engineering application value for fault diagnosis of gearboxes.