Abstract
The traditional fault detection methods have certain detection delay for dynamic processes with strong nonlinearity. In order to increase fault detection rate and decrease the fault detection delay, this paper proposed a new fault isolation and diagnosis method. The faulty and normal samples are separated using moving window Fisher discriminant analysis combining with mean and variance of projection error, then obtain the fault point position by hypothesis testing theory. Furthermore, the projection vector is revised by adding the auxiliary deviation. To identify the fault variables, relative error of variance is presented and compared with traditional complete deposition construction plots method. The simulation results of Tennessee Eastman benchmark process fault data sets show the advantages of this proposed method in fault isolation and diagnosis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhao, C., Wang, W.: Efficient faulty variable selection and parsimonious reconstruction modeling for fault isolation. J. Process Control 38, 31–41 (2016)
Qin, S.J.: Statistical process monitoring: basics and beyond. J. Chemometr. 17, 480–502 (2003)
He, Q.P., Qin, S.J., Wang, J.: A new fault diagnosis method using fault directions in Fisher discriminant analysis. AIChE J. 51(2), 555–571 (2005)
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometr. Intell. Lab. Syst. 50(2), 243–252 (2000)
Downs, J., Vogel, E.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17(3), 245–255 (1993)
Shen, Y., Ding, S.X., Haghani, A., et al.: A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. J. Process Control 22(9), 1567–1581 (2012)
Samuel, R.T., Cao, Y.: Nonlinear process fault detection and identification using kernel PCA and kernel density estimation. Syst. Sci. Control Eng. 4, 165–174 (2016)
Chen, Z., Zhang, K., Ding, S.X., et al.: Improved canonical correlation analysis-based fault detection methods for industrial processes. J. Process Control 41, 26–34 (2016)
Alcala, C.F., Qin, S.J.: Reconstruction-based contribution for process monitoring with kernel principal component analysis. Automatica 45(7), 1593–1600 (2009)
Jiang, B., Huang, D., Zhu, X., et al.: Canonical variate analysis-based contributions for fault identification. J. Process Control 26, 17–25 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tian, H., Jia, L. (2017). Dynamic Process Fault Isolation and Diagnosis Using Improved Fisher Discriminant Analysis and Relative Error of Variance. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_21
Download citation
DOI: https://doi.org/10.1007/978-981-10-6373-2_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6372-5
Online ISBN: 978-981-10-6373-2
eBook Packages: Computer ScienceComputer Science (R0)