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Selection of Steady State Time-Periods for Monitoring an Industrial Heat Exchanger

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Advanced Solutions in Diagnostics and Fault Tolerant Control (DPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 635))

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Abstract

The paper proposes a method to select time-periods in which the measured data conform to steady-state conditions. This selection is the first step of any monitoring method based on estimating parameters of a static model. The proposed method is based on a local polynomial modeling of the data evolution and deals with multivariate data by applying principal component analysis. This method is applied on data collected from an industrial heat exchanger to monitor its heat exchange capacity.

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References

  1. Cao, S., Rhinehart, R.: An efficient method for on-line identification of steady state. J. Process Control 5(6), 363–374 (1995)

    Article  Google Scholar 

  2. Crow, E.L., Davis, F.A., Maxfield, M.W.: Statistics Manual: With Examples Taken from Ordnance Development. Dover, New York (1960)

    MATH  Google Scholar 

  3. Habbi, H., Kinnaert, M., Zelmat, M.: A complete procedure for leak detection and diagnosis in a complex heat exchanger using data-driven fuzzy models. ISA Trans. 48(3), 354–361 (2009). doi:10.1016/j.isatra.2009.01.004

    Article  Google Scholar 

  4. Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, New York (2009)

    Book  MATH  Google Scholar 

  5. Herv, A.: The Bonferonni and idk corrections for multiple comparisons. In: Encyclopedia of Measurement and Statistics. Sage, Thousand Oaks (2007)

    Google Scholar 

  6. Lienhard, J.H.I.V., Lienhard, J.H.V.: A Heat Transfer Textbook, 4th edn. Courier Corporation, New Jersey (2015)

    MATH  Google Scholar 

  7. Jolliffe, I.T: Principal Component Analysis (2nd edn.). Springer (2002)

    Google Scholar 

  8. Kandlikar, S., Shah, R.: Multipass plate heat exchangers - effectiveness-NTU results and guidelines for selecting pass arrangements. J. Heat Transf. 111(2), 300–313 (1989)

    Article  Google Scholar 

  9. Kelly, J.D., Hedengren, J.D.: A steady-state detection (SSD) algorithm to detect non-stationary drifts in processes. J. Process Control 23(3), 326–331 (2013)

    Article  Google Scholar 

  10. Kim, M., Yoon, S.H., Domanski, P.A., Vance Payne, W.: Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner. Int. J. Refrig. 31(5), 790–799 (2008)

    Article  Google Scholar 

  11. Korbel, M., Bellec, S., Jiang, T., Stuart, P.: Steady state identification for on-line data reconciliation based on wavelet transform and filtering. Comput. Chem. Eng. 63, 206–218 (2014)

    Article  Google Scholar 

  12. Rhinehart, R.R.: Automated steady and transient state identification in noisy processes. In: 2013 American Control Conference, pp. 4477–4493 (2013)

    Google Scholar 

  13. Ruiz, G., Castellano, M., Gonzlez, W., Roca, E., Lema, J.: Anaerobic digestion process parameter identification and marginal confidence intervals by multivariate steady state analysis and bootstrap. In: Puigjaner, L., Espua, A. (eds.) Computer Aided Chemical Engineering, vol. 20, pp. 1327–1332. Elsevier (2005)

    Google Scholar 

  14. Delrot, S., Guerra, T.M., Dambrine, M., Delmotte, F.: Fouling detection in a heat exchanger by observer of Takagi Sugeno type for systems with unknown polynomial inputs. Eng. Appl. Artif. Intell. 25(8), 1558–1566 (2012)

    Article  Google Scholar 

  15. Xie, S., Yang, C., Xie, Y., Wang, X.: The steady state detection based on outliers identification for sodium aluminate solution evaporation process. Chinese Automation Congress (CAC) 2015, 281–285 (2015)

    Google Scholar 

  16. Wang, S., Zhou, Q., Xiao, F.: A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energ. Build. 42(4), 477–490 (2010)

    Article  Google Scholar 

  17. Weyer, E., Szederknyi, G., Hangos, K.: Grey box fault detection of heat exchangers. Control Eng. Pract. 8(2), 121–131 (2000)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France

    Yuqi Wang, Jean-Philippe Cassar & Vincent Cocquempot

  2. Veolia Research and Innovation, Limay, France

    Yuqi Wang & Anne-Sophie Guilbert

Authors
  1. Yuqi Wang
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  2. Jean-Philippe Cassar
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  3. Vincent Cocquempot
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  4. Anne-Sophie Guilbert
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Corresponding author

Correspondence to Yuqi Wang .

Editor information

Editors and Affiliations

  1. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Jan M. Kościelny

  2. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Michał Syfert

  3. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Anna Sztyber

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Wang, Y., Cassar, JP., Cocquempot, V., Guilbert, AS. (2018). Selection of Steady State Time-Periods for Monitoring an Industrial Heat Exchanger. In: Kościelny, J., Syfert, M., Sztyber, A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-319-64474-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-64474-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64473-8

  • Online ISBN: 978-3-319-64474-5

  • eBook Packages: EngineeringEngineering (R0)

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