Abstract
Fault isolation is essential to fault monitoring, which can be used to detect the cause of the fault. Commonly used methods include contribution plots, LASSO, Nonnegative garrote, construction-based methods, branch and bound algorithm (B & B), etc. However, these existing methods have shortcomings limiting their implementation when there exist vertical outliers and leverage points, Therefore, to further improve the fault prediction accuracy, this paper present a strategy based on robust nonnegative garrote (R-NNG) variable selection algorithm, which is proved to be robust to outliers in the TE process.
This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61171145 and 61374044.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, C., Wang, W.: Efficient faulty variable selection and parsimonious reconstruction modelling for fault isolation. J. Process Control 38, 31–41 (2016)
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Statist. 32(2), 407–499. (2004). With discussion, and a rejoinder by the authors
Frank, I.E., Friedman, J.H.: A statistical view of some chemometrics regression tools. Technometrics 35(2), 109–135 (1993)
Fu, W.J.: Penalized regressions: the bridge versus the lasso. J. Comput. Graph. Statist. 7(3), 397–416 (1998)
Fan, J., Li, R.: Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Statist. Assoc. 96(456), 1348–1360 (2001)
Breiman, L.: Better subset regression using the nonnegative garrote. Technometrics 37(4), 373–384 (1995)
Yohai, V.J.: High breakdown-point and high efficiency robust estimates for regression. Ann. Statist. 15(2), 642–656 (1987)
Yohai, V.J., Zamar, R.H.: High breakdown-point estimates of regression by means of the minimization of an efficient scale. J. Am. Statist. Assoc. 83(402), 406–413 (1988)
Yuan, M., Lin, Y.: On the non-negative garrote estimator. J. R. Stat. Soc. Ser. B Stat. Methodol. 69(2), 143–161 (2007)
Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17, 245–255 (1993)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley New York (2012)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics, 7, 179–188 (1936)
Wang, J.G., Shieh, S.S., Jang, S.S., Wong, D.S.H., Wu, C.W.: A two-tier approach to the data-driven modeling on thermal efficiency of a BFG/coal co-firing boiler. Fuel 111, 528–534 (2013)
Wang, J.G., Jang, S.S., Wong, D.S.H., Shieh, S.S., Wu, C.W.: Soft-sensor development with adaptive variable selection using nonnegative garrote. Control Eng. Practice 21, 1157–1164 (2013)
Breiman, L.: Better subset regression using the nonnegative garrote. Technometrics 37(4), 373 (1995)
Medina, M.A., Ronchetti, E.: Robust and consistent variable selection for generalized linear and additive models. Technical report. University of Geneva, Switzerland. http://archive-ouverte.unige.ch/unige:36961 (2014)
Antoniadis, A., Gijbels, I., Verhasselt, A.: Variable selection in additive models using P-splines. Technometrics 54(4), 425–438 (2012)
Antoniadis, A., Gijbels, I., Verhasselt, A.: Variable selection in varying-coefficient models using P-splines. J. Comput. Graph. Stat. 21(3), 638–661 (2012)
Gijbels, I., Vrinssen, I.: Robust nonnegative garrote variable selection in linear regression. Comput. Stat. Data Anal. 85, 1–22 (2015)
Kuang, T.H., Yan, Z., Yao, Y.: Multivariate fault isolation via variable selection in discriminant analysis. J. Process Control 35, 30–40 (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
Wang, J. et al. (2017). Multivariate Fault Isolation in Presence of Outliers Based on Robust Nonnegative Garrote. 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_38
Download citation
DOI: https://doi.org/10.1007/978-981-10-6373-2_38
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)