Recursive canonical variate analysis for fault detection of time-varying processes

被引：2

作者：

Shang L.-L. ^{[1
,2
]}

Liu J.-C. ^{[1
]}

Tan S.-B. ^{[1
]}

Wang G.-Z. ^{[1
]}

机构：

[1] School of Information Science & Engineering, Northeastern University, Shenyang

[2] School of Electrical Engineering, Nantong University, Nantong

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2016年 / 37卷 / 12期

关键词：

CVA (canonical variate analysis); Fault detection; First order perturbation theory; Time-varying processes;

D O I：

10.3969/j.issn.1005-3026.2016.12.001

中图分类号：

学科分类号：

摘要：

Because CVA (canonical variate analysis) is unable to adapt the characteristics of time-varying processes, by which the normal changes of the process is easily identified as faults, it is very necessary to propose a monitoring approach for time-varying processes. The exponential weighted moving average approach was adopted to update the covariance of the past observation vectors. The most critical problem faced by recursive CVA algorithm is the high computation cost. To reduce the computation cost, the first order perturbation theory was introduced to update recursively the singular value decomposition (SVD) of the Hankel matrix. The computation cost of recursive SVD based on the first order perturbation theory is significantly less compared to the SVD. Recursive canonical variate analysis based on the first order perturbation (RCVA-FOP) was applied in the Tennessee Eastman chemical process. Simulation results indicate that the proposed method not only can effectively adapt to the normal change of time-varying processes, but also can detect two types of faults. © 2016, Editorial Department of Journal of Northeastern University. All right reserved.

引用

页码：1673 / 1676and1682

共 8 条

[1]

Sammaknejad N., Huang B., Fatehi A., Et al., Adaptive monitoring of the process operation based on symbolic episode representation and hidden Markov models with application toward an oil sand primary separation, Computers & Chemical Engineering, 71, pp. 281-297, (2014)

[2]

Qin S.J., An overview of subspace identification, Computer & Chemical Engineering, 30, 10-12, pp. 1502-1513, (2006)

[3]

Gustafsson T., Recursive system identification using instrumental variable subspace tracking, Proceedings of the 11th IFAC Symposium on System Identification, (1997)

[4]

Oku H., Kimura H., Recursive 4SID algorithms using gradient type subspace tracking, Automatica, 38, 6, pp. 1035-1043, (2002)

[5]

Houtzager I., Van Wingerden J.W., Verhaegen M., Recursive predictor-based subspace identification with application to the real-time closed-loop tracking of flutter, IEEE Transactions on Control Systems Technology, 20, 4, pp. 934-949, (2012)

[6]

Choi S.W., Martin E.B., Morris A.J., Et al., Adaptive multivariate statistical process control for monitoring time-varying processes, Industrial & Engineering Chemistry Research, 45, 9, pp. 3108-3118, (2006)

[7]

Ding S.X., Zhang P., Naik A., Et al., Subspace method aided data-driven design of fault detection and isolation systems, Journal of Process Control, 19, 9, pp. 1496-1510, (2009)

[8]

Naik A.S., Yin S., Ding S.X., Et al., Recursive identification algorithms to design fault detection systems, Journal of Process Control, 20, 8, pp. 957-965, (2010)

← 1 →