Fault Identification of Vehicle Automatic Transmission based on Sparse Autoencoder and Support Vector Machine

摘要：

　　Support vector machine(SVM)got a good classification ability,but the recognition accuracy was easily affected by the value of the kernel parameters.Aiming at this problem,sparse autoencoder(SAE)has its unique advantages in dealing with complex structured data,so the combination of sparse autoencoder and support vector machine(SAE+SVM)was proposed on the fault identification of vehical automatic transmission.Firstly,eight indicators such as engine speed,throttle opening,water temperature and so on are collected from acquisition automobile automatic transmission under 3 running conditions.The data was used as input dataset of the sparse autoencoding model to extract the features.Then the features was used for the fault classification and identification based on support vector machine.Compared with using support vector machine only,the experiment results showed that the recognition accuracy based on the combination of sparse autoencoder and support vector machine(SAE+SVM)was less affected by the value of the kernel parameters and got better recognition accuracy.So the combination of sparse autoencoder and support vector machine can be better used in the real-time fault identification and diagnosis of automatic transmission.

作者: Canyi Du Shaohui Zhang Zusheng Lin Feifei Yu

作者单位: School of Automobile and Transportation Engineering,Guangdong Polytechnic Normal University,Guangzho School of Mechanical and Automotive Engineering,Xiamen University of Technology,Guangzhou,China School of Mechatronic Engineering,Guangdong Polytechnic Normal University,Guangzhou,China

会议类型: 国际会议

会议名称: The 2nd International Symposium on Application of Materials Science and Energy Materials (SAMSE 2018) 第二届材料科学应用与能源材料国际研讨会2018年

会议地点: 上海

会议语种:英文

页码: 1-7

在线出版日期: 2018-12-17（万方平台首次上网日期，不代表论文的发表时间）