基于多任务深度学习的齿轮箱多故障诊断方法  

Multi-fault diagnosis for gearboxes based on multi-task deep learning

作　　者：赵晓平[1] 吴家新[1] 钱承山[1] 张永宏[2] 王丽华[2] ZHAO Xiaoping;WU Jiaxin;QIAN Chengshan;ZHANG Yonghong;WANG Lihua(School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Information and Control,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学计算机与软件学院,南京210044 [2]南京信息工程大学信息与控制学院,南京210044

出　　处：《振动与冲击》2019年第23期271-278,共8页Journal of Vibration and Shock

基　　金：国家自然科学基金(51505234;51575283;51405241)

摘　　要：机械故障诊断领域已进入了“大数据”时代,且深度学习以其强大的自适应特征提取和分类能力也在机械大数据处理方面取得了丰硕的成果。然而这些研究均运用在单标签体系下,诊断单一目标故障。在大数据背景下,单标签体系不仅割裂了机械装备不同目标故障之间的联系,也难以完整描述装备故障位置、类型、程度等种类繁多的健康状态信息。提出了一种基于多任务深度学习模型的诊断方法,对齿轮箱的轴承及齿轮这两种目标的故障同时进行诊断。其优势在于通过单独的任务层,能够从同一信号中自适应的提取不同目标的特征,并进行诊断。实验结果表明,该方法实现了在多种工况,大量样本下对齿轮箱内轴承和齿轮不同故障的准确诊断。The field of mechanical fault diagnosis enters a"big data"era,and the deep learning achieves fruitful results in mechanical big data processing with its powerful adaptive feature extraction and classification capabilities.However,this method is used in a single-label system to diagnose a single target fault.Under the background of big data,the single-label system not only cuts apart connections among different target faults of mechanical equipment,but also is difficult to fully describe lots of equipment fault state information,such as,fault location,type,and degree,etc.Here,a diagnosis method based on the multi-task deep learning model was proposed to simultaneously diagnose faults of bearing and gear in gearbox.It was shown that with this method,features of different targets can adaptively be extracted from the same signal,and then these features are used to perform fault diagnosis through a separate task layer.The test results showed that the proposed method realizes simultaneous correct diagnosis of bearing and gear different faults in gearbox under multiple working conditions and a large number of samples.

关 键 词：机械故障诊断 多任务深度学习 轴承 齿轮 

分 类 号：TH212[机械工程—机械制造及自动化] TH213.3