摘要
为解决故障诊断中标签不足的问题,该文以滚动轴承作为对象提出一种改进的对抗迁移学习模型。该模型通过一维卷积结构提取时间信号特征,能够直接处理时域振动信号,并通过大尺寸卷积核抑制噪声的干扰;在对抗迁移学习的域判别器中采用卷积结构替换全连接神经网络,以对抗训练的方式减少训练数据和测试数据间的分布差异,以提高故障诊断精度。将改进后的模型应用于两个滚动轴承故障诊断案例中,通过添加不同信噪比的噪声信号验证提出的模型具有良好的抗干扰能力,同时以故障分类准确率作为指标,验证该模型具有更高的诊断精度和鲁棒性。
In order to solve the problem of insufficient labels in fault diagnosis,this paper proposes an improved adversarial transfer learning model based on rolling bearings.The model extracts time signal features through a one-dimensional convolution structure,can directly process time-domain vibration signals,and suppress noise interference through a large-size convolution kernel;the convolution structure is used to replace the fully connected nerve in the domain discriminator against transfer learning The network reduces the distribution difference between training data and test data in a way of adversarial training,so as to improve the accuracy of fault diagnosis.The improved model was applied to two rolling bearing fault diagnosis cases.The addition of noise signals with different signal-to-noise ratios verified that the proposed model has good antiinterference ability.At the same time,the correct rate of fault classification is used as an indicator to verify that the model has higher diagnostic accuracy and robustness.
作者
杨健
李立新
廖晨茜
蔡晋辉
曾九孙
YANG Jian;LI Lixin;LIAO Chenxi;CAI Jinhui;ZENG Jiusun(China Jiliang University,Hangzhou 310018,China;CLP Haikang Group Co.,Ltd.,Hangzhou 310000,China)
出处
《中国测试》
CAS
北大核心
2021年第9期15-19,40,共6页
China Measurement & Test
基金
国家重点研发计划资助项目(2018YFF0214701)
国家自然科学基金资助项目(61673358)。
关键词
对抗迁移学习
故障诊断
一维卷积结构
域判别器
adversarial transfer learning
fault diagnosis
one-dimensional convolution
domain discrimination
