摘要
机械设备实际工作过程中正常样本丰富、故障样本匮乏,卷积神经网络在处理这种分布不平衡的数据时对少数类的识别率很低。为解决上述问题,提出一种代价敏感卷积神经网络,首先经过多层卷积和池化运算学习原始监测数据中的机械设备本征性能状态知识;其次通过全连接层将本征性能状态知识映射为机械设备健康状态;最后利用代价敏感损失函数为少数类样本赋予较大的误分类代价,实现对不平衡的机械故障数据的有效分类。为验证所提方法的有效性,使用具有不同不平衡比的刀具数据集和轴承数据集,利用代价敏感卷积神经网络以及主流的分类算法分别测试其对于不平衡数据的分类性能。实验结果表明,所提方法对不平衡数据集中的少数类样本识别率相对于传统卷积神经网络提升了22%以上。
In the actual operation of machinery,the normal data are abundant and the fault data are rare.The recognition rate of the minority class is low when the convolutional neural network is used to process these imbalanced data.To solve this problem,an imbalanced fault diagnosis method for machinery based on the cost sensitive convolutional neural network is proposed.Firstly,the intrinsic performance state knowledge is achieved in raw data of machinery through multi-level convolution and pooling operations.Then,the intrinsic performance state knowledge is mapped to mechanical health by fully connected layer.Finally,the cost sensitive loss function is used to set a large cost to the misclassification of the minority class.The effective classification of mechanical imbalanced data is realized.The proposed method is evaluated by tool monitoring data and bearing monitoring data with different imbalanced ratio.Compared with the traditional convolutional neural networks,experimental results show that the recognition rate of minority samples in imbalanced datasets has been improved by more than 22%.
作者
董勋
郭亮
高宏力
刘宸宇
李磊
Dong Xun;Guo Liang;Gao Hongli;Liu Chenyu;Li Lei(Engineering Research Center of Advanced Driving Energy-saving Technology,Ministry of Education,Southwest Jiaotong University,Chengdu 610031,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2019年第12期205-213,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(VP21ZR1102Y17025,51905452)
中央高校基本科研专项资金(2682017ZDPY09,2682019CX35,2018GF02)项目资助.
关键词
代价敏感
卷积神经网络
不平衡分类
智能故障诊断
cost sensitive
convolutional neural network
imbalanced classification
intelligent fault diagnosis
