关键词: 故障诊断, 农机, 滚动轴承, 连续小波变换, 卷积神经网络, 支持向量机

Abstract: In the fault diagnosis of agricultural machinery rolling bearings, aiming at the nonlinear and non‑stationary characteristics of bearing vibration signals and the not obvious fault characteristics. This paper proposes a novel method based on Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN) and Support Vector Machine (SVM) of rolling bearing fault diagnosis (CWT—CNN—SVM). Firstly, multi‑scale time‑frequency analysis of rolling bearing vibration signals is carried out using CWT to provide more detailed characteristics for subsequent fault diagnosis. Secondly, the extracted time‑frequency graph is used as input to learn the fault feature information by CNN. Then, SVM is used to classify the output results to achieve accurate fault type identification. Compared with CNN, CWT—CNN and CWT—ResNet, the test results show that CWT—CNN—SVM has the highest fault diagnosis accuracy, with a single accuracy of 100% and a 5‑time repeated accuracy of 99.62%. CWT—CNN—SVM not only achieves diagnostic accuracy when dealing with complex rolling bearing fault diagnosis problems, but also shows the advantages of combining deep learning with fault diagnosis, which can further improve the performance of small data sets. The CWT—CNN—SVM method proposed in this paper has beneficial theoretical value and practical application prospect for improving the fault diagnosis performance of agricultural machinery rolling bearings.

Key words: fault diagnosis, agricultural machinery, rolling bearing, continuous wavelet transform, CNN, support vector machine