摘要
运用深度学习技术对滚动轴承微小故障发生的位置、类别和严重程度进行精准自动的辨识是当前故障诊断领域研究的热点。传统的故障诊断方法过度依赖于工程师凭经验进行手工特征提取,难以有效提取微小故障特征。提出了一种改进的CNNs-SVM的新方法用于电机轴承的故障快速智能诊断,该方法采用1×1的过渡卷积层与全局均值池化层的组合代替传统CNN的全连接网络层结构,有效减少CNN的训练参数量,在测试阶段采用支持向量机代替Softmax分类器进一步提升诊断准确率。最后将提出的方法用于电机支撑滚珠轴承的故障实验数据并与多种算法对比验证。结果表明,改进CNNs-SVM算法的故障识别准确率高达99.86%,同时在不同负载下具有良好的迁移泛化能力,具备实际工程应用的可行性。其诊断准确率和测试时间明显优于其他智能算法。
Using deep learning technique to automatically and accurately identify the incipient fault of rolling bearing, especially the fault position, classification and severity degree, is a research hotspot in current fault diagnosis field. The traditional fault diagnosis method excessively relies on the manual feature extraction by the engineers with prior knowledge, which is difficult to effectively extract incipient fault features. In this paper, a novel improved CNNs-SVM method is proposed and used for the rapid intelligent fault diagnosis of motor rolling bearing. This method adopts the combination of 1×1 transitional convolution layer and global average pooling layer to replace the fully connected network layer structure of traditional CNN, which effectively reduces the number of training parameters of CNN. In test stage, the method uses SVM to replace the Softmax classifier, which further improves the diagnosis accuracy. The proposed method was applied to the fault experiment data of the motor support rolling bearing, and the method was compared and verified with traditional intelligent diagnosis methods. The results show that the accuracy of fault identification of the improved CNNs-SVM algorithm reaches up to 99.86%, and the proposed method has good migration generalization ability under different load conditions and possesses the feasibility for practical engineering application. The fault diagnosis accuracy and test time of the method is obviously better than other intelligent algorithms.
作者
宫文峰
陈辉
张美玲
张泽辉
Gong Wenfeng;Chen Hui;Zhang Meiling;Zhang Zehui(Key Laboratory of High Performance Ship Technology of Ministry of Education,Wuhan University of Technology,Wuhan 430063,China;Beihai Campus,Guilin University of Electronic and Technology,Beihai 536000,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第1期195-205,共11页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金面上项目(51579200)
中央高校基本科研业务经费资助武汉理工大学优秀博士学位论文培育项目(2019-YB-023)
广西高校中青年教师科研基础能力提升项目(2019KY0216)
国家留学基金委博士联合培养项目资助.
关键词
故障诊断
卷积神经网络
支持向量机
深度学习
全局均值池化
fault diagnosis
convolutional neural network
support vector machine
deep learning
global average pooling
作者简介
宫文峰,2009年于山东科技大学获得学士学位,2014年于桂林电子科技大学获得硕士学位,现为武汉理工大学与新加坡南洋理工大学联合培养博士研究生,主要研究方向为智能故障诊断与健康状态监测、深度学习与机器学习。E-mail:wfgongcn@163.com;通信作者:陈辉,分别在1984年、1987年和1991年于武汉理工大学获得学士学位、硕士学位和博士学位,现为武汉理工大学博士生导师,国家二级教授,主要研究方向为船舶电力推进及智能船舶技术、电力系统建模与新能源技术等。E-mail:hchen@whut.edu.cn
