Study on Fault Diagnosis of Power-Shift Steering Transmission Based on Spectrometric Analysis and SVM
ZHANG Ying-feng1,2，MA Biao1*，ZHANG Jin-le1，CHEN Man1, FAN Yu-heng3, LI Wen-chang4
1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China 2. Department of Automobile Engineering, Academy of Military Transportation, Tianjin 300161, China 3. Weapons Institute of Science and Technology of China, Beijing 100089, China 4. Jianglu Machinery-Electronics Technology Co., Ltd. Technical Center, Xiangtan 411100, China
Abstract：Spectrometric oil analysis is an important method to study the running state of Power- Shift Steering Transmission (PSST). A method of multiple out least squares support vector regression was developed using spectrometric oil analysis data and SVM(Support Vector Machine). The spectrometric oil analysis data were studied using multiple out least squares support vector regression. It has been proved that the regression data are good in approximation effect for No.1 PSST. And the predictive values for No.2 PSST are highly veracious with the test data. The fault information was found and the fault position was determined through comparative analysis. This method has been proved to have practice significance for finding fault-hidden dangers and judging fault positions.
张英锋1,2，马 彪1*，张金乐1，陈 漫1，范昱珩3，李文昌4 . 基于光谱分析和SVM的综合传动故障诊断研究[J]. 光谱学与光谱分析, 2010, 30(06): 1586-1590.
ZHANG Ying-feng1,2，MA Biao1*，ZHANG Jin-le1，CHEN Man1, FAN Yu-heng3, LI Wen-chang4 . Study on Fault Diagnosis of Power-Shift Steering Transmission Based on Spectrometric Analysis and SVM . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(06): 1586-1590.
[1] WEI Hai-jun，SUN Pei-ting, YIN Feng(魏海军，孙培廷，尹 峰). Transaction of CSICE(内燃机学报)，2005，23(1)：88. [2] LI Zhu-guo, WEI Shi-ming, YU Wu-quan(李柱国，尉世明，俞五全). Chinese Internal Combustion Engine Engineering, 2001, 22(1): 66. [3] ZHENG Chang-song, MA Biao, MA Yuan(郑长松，马 彪，马 源). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2009，29(3)：749. [4] ZHENG Chang-song, MA Biao, WAN Yao-qing(郑长松，马 彪，万耀青). China Mechanical Engineering(中国机械工程)，2006，17(9)：975. [5] Boser Bernhard E，Guyon Isabelle M，Vapnik Vladimir N. A Training Algorithm for Optimal Margin Classifiers. Proceedings of the fifth Annual Workshop Computational Learning Theory，New York, 27-29, July, 1992，144. [6] Vapnik V N. Statistical Learning Theory. New York：John Wiley&Sons，1998. [7] LI Yuan-cheng，FANG Ting-jian，YU Er-keng(李元诚，方廷健，于尔铿). Proceedings of the Chinese Society for Electrical Engineering(中国电机工程学报)，2003，23(6)：55. [8] ZHAI Yong-jie, HAN Pu，WANG Dong-feng(翟永杰，韩 璞，王东风). Proceedings of the Chinese Society for Electrical Engineering(中国电机工程学报)，2003，23(9)：198. [9] Hu Guo-sheng，Ren Guang-yong，Jiang Jing-Jiang. A Support Vector Reduction Method for Accelerating Calculation. Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition，Beijing，China，2-4 Nov.，2007，1408. [10] Suykens J A K，Vandewalle J. IEEE Transactions on Circuits and Systems-I，2000，47(7)：1109. [11] Suykens J A K, Vandewalle J. Neural Processing Letters，1999，9(3)：293. [12] YAN Wei-wu，CHANG Jun-lin，SHAO Hui-he(阎威武，常俊林，邵惠鹤). Journal of Shanghai Jiaotong University(上海交通大学学报)，2004，38 (4) ：521. [13] Chapelle Olivier，Haffner Patrick，Vapnik Vladimir N. IEEE Transactions on Neural Neworks，1999，10(5)，1055. [14] DENG Nai-yang, TIAN Ying-jie(邓乃扬，田英杰). A New Method of Data Mining: Support Vector Machine(数据挖掘中的新方法: 支持向量机). Beijing: Science Press(北京: 科学出版社)，2004. 344.
