A Convolutional Neural Network-Based Recognition Method of Gear Performance Degradation Mode

In an increasingly intelligent modern society, whether in industrial production activities or daily life, mechanical transmission equipment is more and more widely used. Once a failure occurs, it will not only cause the stagnation of industrial production, bring huge economic losses and environmental pollution, but may also cause casualties. Therefore, it is particularly important to identify and monitor the performance degradation of mechanical equipment. Based on the convolutional neural network (CNN), a stacking incremental deformable residual block network recognition model is proposed. This method converts the one-dimensional signal recognition problem into an image recognition problem. The average pooling layer replaces the fully connected layer, and the large-size convolution kernel is replaced with a small-size convolution kernel. With the recognition of the gear performance degradation modes, the experiment proves that the multi-channel recognition model has a better recognition effect.

Automated summary

In an increasingly intelligent modern society, the identification and monitoring of mechanical equipment's performance degradation is crucial. This paper proposes a recognition model based on convolutional neural network (CNN) called stacking incremental deformable residual block network. By converting the one-dimensional signal recognition problem into an image recognition problem, the model achieves better recognition performance.