一种GAF-CNN行星齿轮箱故障诊断方法

为将深度学习识别2D图像的优势应用于行星齿轮箱故障诊断,提出一种格拉姆角场-卷积神经网络行星齿轮箱故障诊断模型.利用格拉姆角场中的格拉姆角差场和格拉姆角和场两种方法,将行星齿轮箱振动信号转化为2D图像,提取图像特征并输入优化后的卷积神经网络模型,最终获得理想的识别精度.通过研究网络参数、不同网络层对故障诊断模型的影响,构建模型的最优组合.试验和对比结果表明,格拉姆角差场卷积神经网络比格拉姆角和场卷积神经网络具有更高的识别精度,在行星齿轮箱故障诊断方面的效果优于其他智能算法.

A Fault Diagnosis Method for Planetary Gearbox Based on GAF-CNN

In order to apply the advantages of deep learning to recognize 2D images for the fault diagnosis of planetary gearboxes, a fault diagnosis model of planetary gearboxes based on gram angle field-convolution neural network (GAF-CNN) was proposed. Using two methods, gram angle difference field (GADF) and Gram angle sum field (GASF) in the gram angle field (GAF), the planetary gearbox vibration signal was converted into a 2D image, and the image features were extracted and input into the optimized CNN model, and the ideal recognition accuracy was finally obtained. Analyzing the influence of network parameters and different network layers on the fault diagnosis model, an optimal model combination was carried out. The test and comparison analysis results show that GADF-CNN can provide higher recognition accuracy than GASF-CNN; GADF-CNN is superior to other intelligent algorithms in the faults diagnosis of planetary gearbox.