期刊文献+

基于支持向量机的ESP系统传感器故障诊断方法 被引量:5

Sensor Fault Diagnosis Method in ESP System with Support Vector Machines
下载PDF
导出
摘要 应用支持向量机(SVMs)回归估计方法建立 ESP 系统的传感器预测模型;将支持向量机模式分类方法应用于传感器的故障分离,用 DAGSVM 作为残差分类器获得故障结果。研究结果表明将支持向量机应用于 ESP 系统的传感器故障诊断是有效可行的。 Sensor forecast models in ESP system are constructed with support vector machines (SVMs) regression algorithm. DAGSVM (Directed Acyclic Graph SVM) classification algorithm is applied to sensors fault isolation. The research result indicates the application of SVMs to sensor fault diagnosis in ESP system is effective and feasible.
出处 《系统仿真学报》 EI CAS CSCD 北大核心 2005年第3期682-684,717,共4页 Journal of System Simulation
基金 上海汽车工业科技发展基金会项目(0224)
关键词 支持向量机 故障检测分离 电子稳定程序 传感器 support vector machines fault detection and isolation ESP sensor
  • 引文网络
  • 相关文献

参考文献8

  • 1VapnikVN.统计学习理论的本质[M].北京:清华大学出版社,2000..
  • 2Pisu Pierluigi, et al. Vehicle Chassis Monitoring System [J]. Control Engineering Practice, 2003, 11(3): 345-354.
  • 3Van Gestel T. Financial Time Series Prediction Using Least Squares Support Vector Machines within the Evidence Framework [J]. IEEE Transactions on Neural Networks, 2001, 12(4): 809-821.
  • 4Vapnik V N. An Overview of Statistical Learning Theory [J]. IEEE Trans. Neural Network, 1999, 10 (5): 988 - 999 .
  • 5Smola A J, Sch·lkopf B. A Tutorial on Support Vector Regression [R]. NeuroCOLT2 Technical Report Series, Royal Holloway College, University of London, UK, 1998.
  • 6Simani S, et al. Diagnosis Techniques for Sensor Faults of Industrial Processes [J]. IEEE Transactions on Control Systems Technology, 2000, 8(5): 848-855.
  • 7N·rgaard M, et al. Neural Networks for Modelling and Control of Dynamic Systems [M]. London: Springer press, 2000.
  • 8Platt J C, et al. Large Margin DAGs for Multiclass Claasification [A]. Scholkopf B, Burges C, and Smola A J, in Advances in Kernel Methods - Support Vector Learning [C]. Cambridge, MA: MIT Press, 1999.

共引文献170

同被引文献74

引证文献5

二级引证文献45

;
使用帮助 返回顶部