Fault isolation using modified contribution plots

Computers and Chemical Engineering

Volumn 61, Issue , 2014, Pages 9-19

  Liu, Jialin ^a   Chen, Ding Sou ^b  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b CHINA STEEL CORPORATION   (Taiwan)

Author keywords

Contribution plots; Fault isolation; Missing data analysis; Principal component analysis

Indexed keywords

CONTINUOUS STIRRED TANK REACTOR; CONTRIBUTION PLOTS; DATA-DRIVEN APPROACH; FAULT ISOLATION; INDUSTRIAL PROCESSS; MISSING DATA ANALYSIS; MULTIPLE SENSORS; PRIORI KNOWLEDGE;

PRINCIPAL COMPONENT ANALYSIS;

INDUSTRIAL APPLICATIONS;

EID: 84887258679     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2013.10.004     Document Type: Article

References (16)

1. Alcala C.F., Qin S.J. Reconstruction-based contribution for process monitoring. Automatica 2009, 45:1593-1600.
2. Dunia R., Qin S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal 1998, 44:1813-1831.
3. Hopkins, R.W., Miller, P., Swanson, R.E., & Scheible, J.J. (1995). Method of controlling a manufacturing process using multivariate analysis. US Patent 5442562.
4. Jackson J.E. A user's guide to principal components 1991, Wiley, New York.
5. Kariwala V., Odiowei P.E., Cao Y., Chen T. A branch and bound method for isolation of faulty variables through missing variable analysis. Journal of Process Control 2010, 20:1198-1206.
6. Kourti T., MacGregor J.F. Multivariate SPC methods for process and product monitoring. Journal of Quality Technology 1996, 28:409-428.
7. Liu J., Chen D.S. Fault detection and identification using modified Bayesian classification on PCA subspace. Industrial and Engineering Chemistry Research 2009, 48:3059-3077.
8. Qin S.J., Yue H., Dunia R. Self-validating inferential sensors with application to air emission monitoring. Industrial and Engineering Chemistry Research 1997, 36:1675-1685.
9. Qin S.J., Valle S., Piovoso M.J. On unifying multiblock analysis with application to decentralized process monitoring. Journal of Chemometrics 2001, 15:715-742.
10. Qin S.J. Statistical process monitoring: basics and beyond. Journal of Chemometrics 2003, 10:463-482.
11. Singhal A., Seborg D.E. Pattern matching in multivariate time series databases using a moving-window approach. Industrial and Engineering Chemistry Research 2002, 41:3822-3838.
12. Westerhuis J.A., Gurden S.P., Smilde A.K. Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems 2000, 51:95-114.
13. Yoon S., MacGregor J.F. Statistical and causal model-based approaches to fault detection and isolation. AIChE Journal 2000, 46:1813-1824.
14. Yoon S., MacGregor J.F. Fault diagnosis with multivariate statistical models part I: Using steady state fault signatures. Journal of Process Control 2001, 11:387-400.
15. Yue H.H., Qin S.J. Reconstruction-based fault identification using a combined index. Industrial and Engineering Chemistry Research 2001, 40:4403-4414.
16. Zhao C., Sun Y., Gao F. A multiple-time-region (MTR)-based fault subspace decomposition and reconstruction modeling strategy for online fault diagnosis. Industrial and Engineering Chemistry Research 2012, 10.1021/ie301096x.