基于DPCA残差互异度的故障检测与诊断方法

Fault Detection and Diagnosis Based on Residual Dissimilarity in Dynamic Principal Component Analysis

针对动态主元分析方法中残差自相关性降低过程故障检测率问题,提出基于动态主元分析残差互异度的故障检测与诊断方法.首先,应用动态主元分析(Dynamic principal component analysis,DPCA)计算动态过程数据的残差得分;接下来,应用滑动窗口技术并结合互异度指标(Dissimilarity)来监控过程残差得分状态;最后,利用基于变量贡献图的方法进行过程故障诊断分析.本文方法通过DPCA捕获过程的动态特征,同时互异度指标区别于传统的平方预测误差(Square prediction error,SPE),它可以有效地对具有自相关性的残差得分进行过程状态监控.通过一个数值例子和Tennessee Eastman(TE)过程的仿真实验并与传统方法对比分析,仿真结果进一步证实了本文方法的有效性.

Aiming at the problem of reducing process fault detection rate because of residual autocorrelation in dynamic principal component analysis,a novel fault detection and diagnosis based on residual dissimilarity in dynamic principal component analysis is proposed in this paper.Firstly,Dynamic principal component analysis(DPCA)is used to calculate residual score of a dynamic process.Next,moving window technology and dissimilarity index are utilized to monitor the status of this process in residual score.Finally,a fault diagnosis method based on contribution chart of monitored variables is used for discovering the reason causing abnormal change of this process.The proposed method to capture dynamic characteristics of a process through DPCA,meanwhile,the proposed dissimilarity index,which is different from the conventional squared prediction error(SPE),can effectively monitor the process in which the residual scores contain significant autocorrelation.The effectiveness of DPCA-Diss is tested in a numerical case and the Tennessee Eastman(TE)process.