Fault Prediction Model Based on Phase Space Reconstruction and Least Squares Support Vector Machine

摘要：

Combining phase space reconstruction theory and least squares support vector machines (LSSVM) method, a novel fault prediction model is proposed in this paper. The model reconstructs phase space for fault characteristics time series of the system and fit the nonlinear relationship of phase point evolution by use of least squares support vector machines according to the laws of phase space evolution. Fault prediction model based on gyroscope drift time series is established for single-step and multi-steps prediction compared with RBF neural network prediction results. The results show that phase space reconstruction method can effectively determine the input and output vectors of prediction model, and in the case of limited samples, the fault prediction model established by the least squares support vector machine has better accuracy and stronger generalization ability.

关键词： phase space reconstruction least squares support vector machines fault prediction model gyroscope drift

作者: Yunhong GAO Yibo LI

作者单位: College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing, China College of Automation Engineering Shenyang Institute of Aeronautical Engineering Shenyang, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-4

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）