Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes

Proceedings of the American Control Conference

Volumn , Issue , 2014, Pages 3500-3505

  Li, Gang ^a   Qin, S Joe ^a   Chai, Tianyou ^b  

^a University of Southern California   (United States)

^b STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

Author keywords

dynamic principal component analysis; multi directional reconstruction based contribution; Root cause diagnosis; smearing effect

Indexed keywords

CONTROL;

AUGMENTED VARIABLES; CONTINUOUS STIRRED TANK REACTOR (CSTR) PROCESS; CONTRIBUTION ANALYSIS; DYNAMIC PRINCIPAL COMPONENT ANALYSIS; MULTI-DIRECTIONAL RECONSTRUCTION BASED CONTRIBUTION; ROOT CAUSE; ROOT CAUSE IDENTIFICATION; SMEARING EFFECT;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84905715757     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.2014.6859002     Document Type: Conference Paper

References (18)

1. S. J. Qin, "Survey on data-driven industrial process monitoring and diagnosis", Annual Reviews in Control, vol. 36, pp. 220-234, 2012.
2. W. Ku, R. Storer, and C. Georgakis, "Disturbance detection and isolation by dynamic principal component analysis", Chemometrics and intelligent laboratory systems, vol. 30, no. 1, pp. 179-196, 1995.
3. A. Raich and A. Cinar, "Statistical process monitoring and disturbance diagnosis in multivariable continuous processes", AIChE Journal, vol. 42, no. 4, pp. 995-1009, 1996.
4. S. Qin and W. Li, "Detection, identification, and reconstruction of faulty sensors with maximized sensitivity", AIChE Journal, vol. 45, no. 9, pp. 1963-1976, 1999.
5. S. Yoon and J. F. MacGregor, "Fault diagnosis with multivariate statistical models Part I: using steady state fault signatures", Journal of Process Control, vol. 11, no. 4, pp. 387-400, 2001.
6. H. H. Yue and S. J. Qin, "Reconstruction-Based Fault Identification Using a Combined Index", Industrial and Engineering Chemistry Research, vol. 40, no. 20, pp. 4403-4414, 2001.
7. J. F. MacGregor, C. Jaeckle, C. Kiparissides, and M. Koutoudi, "Process monitoring and diagnosis by multiblock PLS methods", AIChE Journal, vol. 40, no. 5, pp. 826-838, 1994.
8. J. A. Westerhuis, S. P. Gurden, and A. K. Smilde, "Generalized contribution plots in multivariate statistical process monitoring", Chemometrics and Intelligent Laboratory Systems, vol. 51, no. 1, pp. 95-114, 2000.
9. C. Alcala and S. Qin, "Reconstruction-based Contribution for Process Monitoring", Automatica, vol. 45, no. 7, pp. 1593-1600, 2009.
10. G. Li, C. F. Alcala, S. J. Qin, and D. Zhou, "Generalized reconstructionbased contributions for output-relevant fault diagnosis with application to the tennessee eastman process", Control Systems Technology, IEEE Transactions on, vol. 19, no. 5, pp. 1114-1127, 2011.
11. V. Kariwala, P.-E. Odiowei, Y. Cao, and T. Chen, "A branch and bound method for isolation of faulty variables through missing variable analysis", Journal of Process Control, vol. 20, no. 10, pp. 1198-1206, 2010.
12. J. Liu, "Fault diagnosis using contribution plots without smearing effect on non-faulty variables", Journal of Process Control, vol. 22, no. 9, pp. 1609-1623, 2012.
13. G. Li, K. Peng, J. Ma, and D. Zhou, "A new usage of reconstruction based contributions for quality-related fault diagnosis", in 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013, pp. 4297-4302.
14. S. Valle, W. Li, and S. Qin, "Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods?" Ind. Eng. Chem. Res, vol. 38, no. 11, pp. 4389-4401, 1999.
15. G. Li, S. Joe Qin, and D. Zhou, "Output relevant fault reconstruction and fault subspace extraction in total projection to latent structures models", Industrial & Engineering Chemistry Research, vol. 49, no. 19, pp. 9175-9183, 2010.
16. C. F. Alcala and S. Joe Qin, "Analysis and generalization of fault diagnosis methods for process monitoring", Journal of Process Control, vol. 21, no. 3, pp. 322-330, 2011.
17. S. Yoon and J. F. MacGregor, "Fault diagnosis with multivariate statistical models part i: using steady state fault signatures", Journal of process control, vol. 11, no. 4, pp. 387-400, 2001.
18. G. Li, S. Qin, Y. Ji, and D. Zhou, "Reconstruction based fault prognosis for continuous processes", Control Engineering Practice, vol. 18, pp. 1211-1219, 2010.