CIESC Journal

• 过程系统工程 • 上一篇    下一篇

基于SPM的多变量连续过程在线故障预测方法

李钢;周东华   

  1. 清华大学自动化系
  • 出版日期:2008-07-05 发布日期:2008-07-05

SPM-based online fault prediction approach for multivariate continuous processes

LI Gang;ZHOU Donghua

  

  • Online:2008-07-05 Published:2008-07-05

摘要: 研究了一类模型未知带有隐含故障的多变量连续过程故障预测问题。基于统计过程监测(SPM)方法,提出了一种上述故障预测问题的解决方案。该方案首先利用正常状态下的样本数据建立主成分分析(PCA)模型,然后根据该模型构造出预测特征量,最后对该特征量进行时间序列分析和预测,从而预测出系统的剩余有效寿命(RUL)。针对线性时不变系统构造了预测特征量,并分析了在一定的系统结构假设和故障假设下的剩余有效寿命预测误差。基于CSTR的仿真实例说明了该方法的有效性。

Abstract:

Fault prediction for a class of unknown-model multivariate continuous processes with a hidden fault was studied,and a solution was given based on statistical process monitoring(SPM)approach. A principle component analysis(PCA)model using sample data under normal state was built,then the characteristic value for fault prediction was constructed,and time series analysis and prediction were applied to the characteristic value to predict the remaining useful life(RUL)of the system. Aiming at the linear time invariant system,a characteristic value was proposed and the prediction error of RUL was analyzed under some assumptions for system structure and hidden fault. A case study on a CSTR showed the efficiency of the proposed approach.