A New Sensor Fault Diagnosis Technique Based Upon Subspace Identification and Residual Filtering

  • Srinivasan Rajaraman
  • Uwe Kruger
  • M. Sam Mannan
  • Juergen Hahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)


This paper presents a new methodology for designing a detection, isolation, and identification scheme for sensor faults in linear time-varying systems. Practically important is that the proposed methodology is constructed on the basis of historical data and does not require a priori information to isolate and identify sensor faults. This is achieved by identifying a state space model and designing a fault isolation and identification filter. To address time-varying process behavior, the state space model and fault reconstruction filter are updated using a two-time-scale approach. Fault identification takes place at a higher frequency than the adaptation of the monitoring scheme. To demonstrate the utility of the new scheme, the paper evaluates its performance using simulations of a LTI system and a chemical process with time-varying parameters and industrial data from a debutanizer and a melter process.


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  1. 1.
    Russell, E.L., Chiang, L.H., Braatz, R.: Data-Driven Techniques for Fault Detection and Diagnosis in Chemical Processes. Springer-Verlag, New York (2000)Google Scholar
  2. 2.
    Rajaraman, S., Hahn, J., Mannan, M.S.: A methodology for fault detection, isolation and identification for nonlinear processes with parametric uncertainties. Industrial & Engineering Chemistry Research 43(21), 6774–6786 (2004)CrossRefGoogle Scholar
  3. 3.
    van Overschee, P.V., de Moor, B.D.: Subspace Identification For Linear Systems - Theory, Implementation and Applications. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  4. 4.
    Qin, S.J., Li, W.: Detection, identification, and reconstruction of faulty sensors wiht maximized sensitivity. AIChE Journal 45, 1963 (1999)CrossRefGoogle Scholar
  5. 5.
    Qin, S.J., Li, W.: Detection, identification of faulty sensors in dynamic processes. AIChE Journal 47, 1581–1593 (2001)CrossRefGoogle Scholar
  6. 6.
    Li, W., Raghavan, H., Shah, S.: Subspace identification of continuous time models for process fault detection and isolation. Journal of Process Control 13, 407–421 (2003)CrossRefGoogle Scholar
  7. 7.
    Treasure, R.J., Kruger, U., Cooper, J.E.: Dynamic multivariate statistical process control using subspace identification. Journal of Process Control 14, 279–292 (2004)CrossRefGoogle Scholar
  8. 8.
    Chen, J., Patton, R.: Robust Model Based Fault Diagnosis For Dynamic Systems. Kluwer Academic Publishers, Dordrecht (1999)zbMATHGoogle Scholar
  9. 9.
    Ding, X., Frank, P.M.: Fault detection via factorization approach. Systems & Control Letters 14, 431 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Dudzic, M.S., Vaculik, V.: On-line industrial implementation of process monitoring and control applications using multivariate statistical technologies: Challenges and opportunities. In: CD Proceedings of the 7th International Conference on Dynamics and Control of Process Systems (DYCOPS-7), Cambridge,Massachussets, U.S.A (2004)Google Scholar
  11. 11.
    Wang, X., Kruger, U., Lennox, B.: Recursive partial least squares algorithms for monitoring complex industrial processes. Control Engineering Practice 11, 603–632 (2003)Google Scholar
  12. 12.
    Chen, Q., Wyne, R., Goulding, P.R., Sandoz, D.J.: The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice 8(5), 531–543 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Srinivasan Rajaraman
    • 1
    • 2
  • Uwe Kruger
    • 3
  • M. Sam Mannan
    • 1
  • Juergen Hahn
    • 2
  1. 1.Department of Chemical EngineeringTexas A&M UniversityCollege StationU.S.A.
  2. 2.Mary Kay O’ Connor Process Safety CenterTexas A&M UniversityCollege StationU.S.A.
  3. 3.Intelligent Systems and Control GroupQueen’s University BelfastU.K.

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