结合WGAN-GP与CNN-SVM的滚动轴承故障红外诊断
Infrared diagnosis of rolling bearing faults based on WGAN-GP and CNN-SVM
周建民 1沈熙闻 1刘露露1
作者信息
- 1. 华东交通大学江西省载运工具与装备重点实验室,江西南昌 330000;华东交通大学机电与车辆工程学院,江西南昌 330000
- 折叠
摘要
针对实际工程应用中由于滚动轴承故障状态出现的时间很短而导致数据集不平衡难以采用深度学习算法进行故障诊断的问题,提出了一种基于Wasserstein距离的梯度惩罚生成对抗网络(WGAN-GP)和基于支持向量机分类的卷积神经网络(CNN-SVM)相结合的滚动轴承故障红外诊断方法.从红外热像图中构建不平衡数据集,通过采用WGAN-GP对不平衡数据扩充以达到数据集均衡,之后将CNN-SVM模型应用于数据集,提取样本深度特征完成故障分类.实验表明,WGAN-GP与CNN-SVM相结合的模型在不平衡数据集下表现良好,相较于其他模型有更好的故障诊断能力,并且在故障分类阶段的用时可减少16.89％以上.
Abstract
In practical engineering applications,the short duration of rolling bearing fault states leads to imbalanced datasets,making it difficult to use deep learning algorithms for fault diagnosis.In this paper,a n infrared diagnosis method for rolling bearing faults based on the combination of the Wasserstein distance-based gradient penalty genera-tive adversarial network(WGAN-GP)and a support vector machine-based convolutional neural network(CNN-SVM)is proposed.The imbalanced dataset is constructed from infrared thermal images,and WGAN-GP is used to augment the imbalanced data to achieve dataset balance,after which the CNN-SVM model is then applied to the dataset to ex-tract deep features and complete fault classification.The experimental results show that the model combining WGAN-GP with CNN-SVM performs well under imbalanced datasets,with better fault diagnosis capability compared to other models,and reduces the time spent in the fault classification stage by more than 16.89％.
关键词
滚动轴承/故障诊断/不平衡数据集/生成对抗网络Key words
rolling bearings/fault diagnosis/imbalanced dataset/generative adversarial network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据