Fault Prediction for Nonlinear Time Series Based on Temporal Pattern Estimation

被引：0
|
作者：
Su, Shengchao [1 ]
Fan, Xiaolan [1 ]
机构：
[1] Shanghai Univ Engn Sci, Ind Engn Training Ctr, Shanghai 201620, Peoples R China
来源：
FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6 | 2012年 / 121-126卷
关键词：
Temporal pattern estimation; Nonlinear time series; Fault prognosis; Least Squares Support Vector Machines;
D O I：
10.4028/www.scientific.net/AMM.121-126.4471
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
An on-line Least Square Support Vector Regression (LS-SVR) algorithm is given in this paper. Based on this algorithm the method of on-line fault prediction by temporal pattern estimation is proposed. The method needs neither the model to approximate the true system nor the fault training data and primary knowledge. It can study and predict while system's running, and it is believed with fast speed, fewer amounts of calculation and better real-time capability. The result of simulation on CSTR proved the efficiency.
引用
收藏
页码：4471 / 4475
页数：5
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