Research on the Gearbox Fault Diagnosis Method Based on Multi-Model Feature Fusion

Abstract

The gearbox is an important component of rotating machinery and is of great significance for gearbox fault diagnosis. In this paper, a gearbox fault diagnosis model based on multi-model feature fusion was proposed that addressed the limitations of a single or few features reflecting the gearbox’s fault state. The time–frequency feature of the vibration signal was extracted, and the sensitive feature was selected. The sensitive features were extracted using a one-dimensional convolutional neural network. The parallel fusion method was used to fuse the two domain features as inputs to the support vector machine model. The radial basis kernel function and penalty factor of the support vector machine were optimized by improving the particle swarm optimization algorithm. Finally, the gearbox states were identified using the optimized support vector machine model. The results show that the recognition rate of the proposed model is 98.3%, which is higher than that of other models.