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Chemical Process Fault Diagnosis Based on Sensor Validation Approach

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7196)

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.

Keywords

  • Fault detection and diagnosis
  • Principal component analysis
  • Contribution plots
  • Missing data analysis

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References

  1. Kourti, T., MacGregor, J.F.: Multivariate SPC Methods for Process and Product Monitoring. J. Qual. Technol. 28, 409–428 (1996)

    Google Scholar 

  2. 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)

    CrossRef  Google Scholar 

  3. Yoon, S., MacGregor, J.F.: Statistical and Causal Model-Based Approaches to Fault Detection and Isolation. AIChE J. 46, 1813–1824 (2000)

    CrossRef  Google Scholar 

  4. Raich, A., Çinar, A.: Statistical Process Monitoring and Disturbance Diagnosis in Multivariable Continuous Processes. AIChE J. 42, 995–1009 (1996)

    CrossRef  Google Scholar 

  5. Dunia, R., Qin, S.J.: Subspace Approach to Multidimensional Fault Identification and Reconstruction. AIChE J. 44, 1813–1831 (1998)

    CrossRef  Google Scholar 

  6. Yue, H.H., Qin, S.J.: Reconstruction-Based Fault Identification Using a Combined Index. Ind. Eng. Chem. Res. 40, 4403–4414 (2001)

    CrossRef  Google Scholar 

  7. Alcala, C.F., Qin, S.J.: Reconstruction-based Contribution for Process Monitoring. Automatica 45, 1593–1600 (2009)

    CrossRef  MathSciNet  MATH  Google Scholar 

  8. 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)

    CrossRef  Google Scholar 

  9. Liu, J., Chen, D.S.: Fault Detection and Identification Using Modified Bayesian Classification on PCA Subspace. Ind. Eng. Chem. Res. 48, 3059–3077 (2009)

    CrossRef  Google Scholar 

  10. 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)

    CrossRef  Google Scholar 

  11. Jackson, J.E.: A User’s Guide to Principal Components. Wiley, New York (1991)

    CrossRef  MATH  Google Scholar 

  12. Qin, J.S., Valle, S., Piovoso, M.J.: On Unifying Multiblock Analysis with Application to Decentralized Process Monitoring. J. Chemom. 15, 715–742 (2001)

    CrossRef  Google Scholar 

  13. 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)

    CrossRef  Google Scholar 

  14. Wang, X., Kruger, U., Irwin, G.W.: Process Monitoring Approach Using Fast Moving Window PCA. Ind. Eng. Chem. Res. 44, 5691–5702 (2005)

    CrossRef  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28486-1

  • Online ISBN: 978-3-642-28487-8

  • eBook Packages: Computer ScienceComputer Science (R0)