基于GAF与GoogLeNet的轴承故障诊断研究

为提高滚动轴承故障识别准确率,同时避免繁琐的频谱分析,提出基于GAF与GoogLeNet的轴承故障诊断模型.在实验室中采集滚动轴承正常、内环故障、外环故障和滚动体故障4种工况下的振动信号,利用EMD对振动信号进行分解并提取累积贡献90％的分量;基于重叠采样原理,利用格拉姆算法将选择的EMD分量和原始振动信号处理为二维图...

Research on Bearing Fault Diagnosis Based on GAF and GoogLeNet

In order to improve the accuracy of rolling bearing fault diagnosis and avoid tedious spectrum analysis, a bearing fault diagnosis model based on GAF and GoogLeNet was proposed. The vibration signals of normal rolling bearing,inner ring failure,outer ring fault and rolling fault were collected in the laboratory,and the EMD was used to decompose the vibration signal and extract the components contributed 90% of the total; based on the overlapping sampling principle, the selected EMD component and the original vibration signal were processed into a two-dimensional picture by using the Gram algorithm, and these pictures were divided into training set, verification set and test set; the GoogLeNet model was used to learn from the training set, and the trained GoogLeNet model was used to test bearing failure samples. The results show that the bearing fault samples can be better identified by using the GoogLeNet model under the data set constructed by GAF.