基于MTF和改进残差网络的轴承故障定量诊断方法

Method for Bearing Fault Quantitative Diagnosis Based on MTF and Improved Residual Network

有别于目前滚动轴承故障诊断多集中在定性分析阶段,提出了一种使用图像分类的滚动轴承故障定量诊断方法.采用重叠采样方法,对一维时序数据进行数据增强,使用马尔可夫转换场(Markov transition field,MTF)方法将一维时序数据转换成二维图像,为输入到神经网络模型中提供二维图像样本并保留了时域信息,搭建和训练基于迁移学习微调处理的ResNeXt和ResNeSt改进残差网络,将故障图像进行分类并实现故障诊断.采用混淆矩阵和t分布领域嵌入(t-distributed stochastic neighbor embedding,t-SNE)可视化方法进行实验,结果表明,该滚动轴承故障定量诊断方法能够实现多工况滚动轴承故障的定量诊断,且具有诊断精度高和训练速度快的优点.

Different from the current rolling bearing fault diagnosis which mainly focuses on the qualitative analysis stage,a quantitative fault diagnosis method for rolling bearings based on image classification is proposed.The overlapping sampling method is used to enhance the one‑dimensional time series data,and then the Markov transition field(MTF)method is used to convert the one‑dimensional time series data into two‑dimensional images,which provide two‑dimensional image samples for inputting into the neural network model and retain the time‑domain information.The ResNeXt and ResNeSt modified residual networks with fine‑tuning processing based on transfer learning are built and trained to classify fault images and realize fault diagnosis.The confusion matrix method and t‑distributed stochastic neighbor embedding(t‑SNE)visualization method are used to carry out experiments.The results show that the proposed method for rolling bearing fault diagnosis can realize the quantitative diagnosis of multi‑working condition rolling bearing fault,and has higher diagnosis accuracy and faster training speed.