东南大学学报（英文版）2019，Vol.35Issue(1)：36-42,7.DOI:10.3969/j.issn.1003-7985.2019.01.006
GA-1DLCNN方法及其在轴承故障诊断中的应用
GA-1DLCNN method and its application in bearing fault diagnosis
摘要
Abstract
Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural network model (LetNet-5), one-dimensional large-kernel convolution neural network (1 DLCNN) is designed. Since the hyper-parameters of 1 DLCNN have a greater impact on network performance, the genetic algorithm (GA) is used to optimize the hyper-parameters, and the method of optimizing the parameters of 1 DLCNN by the genetic algorithm is named GA-1 DLCNN. The experimental results show that the optimal network model based on the GA-1 DLCNN method can achieve 99.9％ fault diagnosis accuracy, which is much higher than those of other traditional fault diagnosis methods. In addition, the 1 DLCNN is compared with one-dimencional small-kernel convolution neural network (1 DSCNN) and the classical two-dimensional convolution neural network model. The input sample lengths are set to be 128, 256, 512, 1 024, and 2 048, respectively, and the final diagnostic accuracy results and the visual scatter plot show that the effect of 1 DLCNN is optimal.关键词
一维卷积神经网络/大尺寸卷积核/超参数寻优/遗传算法Key words
one-dimensional convolution neural network/large-size convolution kernel/hyper-parameter optimization/genetic algorithm分类
机械制造引用本文复制引用
杨振波,贾民平..GA-1DLCNN方法及其在轴承故障诊断中的应用[J].东南大学学报（英文版）,2019,35(1):36-42,7.基金项目
The National Natural Science Foundation of China (No.51675098) （No.51675098）
