Abstract
Investigating the root causes of abnormal events is a crucial task for an industrial chemical process. When process faults are detected, isolating the faulty variables provides additional information for investigating the root causes of the faults. Numerous data-driven approaches require the datasets of known faults, which may not exist for some industrial processes, to isolate the faulty variables. The contribution plot is a popular tool to isolate faulty variables without a priori knowledge. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect to non-faulty variables was derived. An industrial example, correctly isolating faulty variables and diagnosing the root causes of the faults for the compression process, was provided to demonstrate the effectiveness of the proposed approach for industrial processes.
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References
Kourti, T., MacGregor, J.F.: Multivariate SPC Methods for Process and Product Monitoring. J. Qual. Technol. 28, 409–428 (1996)
Westerhuis, J.A., Gurden, S.P., Smilde, A.K.: Generalized Contribution Plots in Multivariate Statistical Process Monitoring. Chemom. Intell. Lab. Syst. 51, 95–114 (2000)
Yoon, S., MacGregor, J.F.: Statistical and Causal Model-Based Approaches to Fault Detection and Isolation. AIChE J. 46, 1813–1824 (2000)
Raich, A., Çinar, A.: Statistical Process Monitoring and Disturbance Diagnosis in Multivariable Continuous Processes. AIChE J. 42, 995–1009 (1996)
Dunia, R., Qin, S.J.: Subspace Approach to Multidimensional Fault Identification and Reconstruction. AIChE J. 44, 1813–1831 (1998)
Yue, H.H., Qin, S.J.: Reconstruction-Based Fault Identification Using a Combined Index. Ind. Eng. Chem. Res. 40, 4403–4414 (2001)
Alcala, C.F., Qin, S.J.: Reconstruction-based Contribution for Process Monitoring. Automatica 45, 1593–1600 (2009)
He, Q.P., Qin, S.J., Wang, J.: A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis. AIChE J. 51, 555–571 (2005)
Liu, J., Chen, D.S.: Fault Detection and Identification Using Modified Bayesian Classification on PCA Subspace. Ind. Eng. Chem. Res. 48, 3059–3077 (2009)
Kariwalaa, V., Odiowei, P.E., Cao, Y., Chen, T.: A Branch and Bound Method for Isolation of Faulty Variables through Missing Variable Analysis. J. Proc. Cont. 20, 1198–1206 (2010)
Jackson, J.E.: A User’s Guide to Principal Components. Wiley, New York (1991)
Qin, J.S., Valle, S., Piovoso, M.J.: On Unifying Multiblock Analysis with Application to Decentralized Process Monitoring. J. Chemom. 15, 715–742 (2001)
Qin, S.J., Yue, H., Dunia, R.: Self-Validating Inferential Sensors with Application to Air Emission Monitoring. Ind. Eng. Chem. Res. 36, 1675–1685 (1997)
Wang, X., Kruger, U., Irwin, G.W.: Process Monitoring Approach Using Fast Moving Window PCA. Ind. Eng. Chem. Res. 44, 5691–5702 (2005)
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Liu, J. (2012). Chemical Process Fault Diagnosis Based on Sensor Validation Approach. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_7
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DOI: https://doi.org/10.1007/978-3-642-28487-8_7
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