Fault detection with principal component pursuit method

12th European Workshop on Advanced Control and Diagnosis

Pan, Yijun^1 ; Yang, Chunjie^1 ; Sun, Youxian^1 ; An, Ruqiao^1 ; Wang, Lin^1

Department of Control Science and Engineering, University of Zhejiang, Hangzhou Zhejiang, China^1

关键词: Correlation coefficient;    Data-driven approach;    Industrial processs;    Low-rank matrices;    On-line fault detection;    Principal Components;    Process reconstruction;    Tennessee Eastman;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012035/pdf
DOI  :  10.1088/1742-6596/659/1/012035

来源: IOP

【 摘 要 】

Data-driven approaches are widely applied for fault detection in industrial process. Recently, a new method for fault detection called principal component pursuit(PCP) is introduced. PCP is not only robust to outliers, but also can accomplish the objectives of model building, fault detection, fault isolation and process reconstruction simultaneously. PCP divides the data matrix into two parts: a fault-free low rank matrix and a sparse matrix with sensor noise and process fault. The statistics presented in this paper fully utilize the information in data matrix. Since the low rank matrix in PCP is similar to principal components matrix in PCA, a T2statistic is proposed for fault detection in low rank matrix. And this statistic can illustrate that PCP is more sensitive to small variations in variables than PCA. In addition, in sparse matrix, a new monitored statistic performing the online fault detection with PCP-based method is introduced. This statistic uses the mean and the correlation coefficient of variables. Monte Carlo simulation and Tennessee Eastman (TE) benchmark process are provided to illustrate the effectiveness of monitored statistics.