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 onWGAN GP and CNN SVM

作　　者：周建民[1,2] 沈熙闻 刘露露 ZHOU Jian-min;SHEN Xi-wen;LIU Lu-lu(Key Laboratory of Transportation Equipment and Logistics of Jiangxi Province,East China Jiaotong University,Nanchang 330000,China;School of Mechanicals and Vehicle Engineering,East China Jiaotong University,Nanchang 330000,China)

机构地区：[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 [2]华东交通大学机电与车辆工程学院,江西南昌330000

出　　处：《激光与红外》2024年第3期416-422,共7页Laser & Infrared

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。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 generative 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 extract 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 

分 类 号：TN219[电子电信—物理电子学] TP73[自动化与计算机技术—检测技术与自动化装置]