A Data-Driven Process Monitoring Approach with Disturbance Decoupling*

This paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. The performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.

[1]  S. Ding,et al.  Closed-loop subspace identification: an orthogonal projection approach , 2004 .

[2]  Kai Zhang,et al.  An alternative data-driven fault detection scheme for dynamic processes with deterministic disturbances , 2017, J. Frankl. Inst..

[3]  Si-Zhao Joe Qin,et al.  An overview of subspace identification , 2006, Comput. Chem. Eng..

[4]  B. Moor,et al.  Subspace state space system identification for industrial processes , 1998 .

[5]  Kai Zhang,et al.  A data-driven fault detection approach for static processes with deterministic disturbances , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[6]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .

[7]  Jianbin Qiu,et al.  Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method , 2017, IEEE Transactions on Cybernetics.

[8]  Ping Zhang,et al.  Subspace method aided data-driven design of fault detection and isolation systems , 2009 .

[9]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[10]  Yong Zhang,et al.  Data-driven realizations of kernel and image representations and their application to fault detection and control system design , 2014, Autom..

[11]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[12]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[13]  Bart De Moor,et al.  Subspace Identification for Linear Systems: Theory ― Implementation ― Applications , 2011 .

[14]  Richard D. Braatz,et al.  Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes , 2000 .

[15]  B. De Moor,et al.  Closed loop subspace system identification , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[16]  Tohru Katayama,et al.  Subspace Methods for System Identification , 2005 .