Data-Driven Design of Observer-Based Fault Diagnosis Systems

  • Steven X. DingEmail author
Part of the Advances in Industrial Control book series (AIC)


Motivated by the fact that the residual generators introduced in the previous chapter are open-loop configurated and thus less robust and involved for the online computation, we present, in this chapter, schemes for the construction/realization of observer-based fault diagnosis systems with the aid of the data-driven realization of kernel representation \(\varPsi _{s}^{\perp }\) given in Algorithm 9.3. As known, observer-based residual generators are closed-loop configurated with a feedback of the residual signals and the involved computations are realized in a recursive form. By a suitable design, they are of high robustness and demand less online computational capacity.


Kalman Filter Fault Detection Parity Vector Residual Signal Sensor Fault 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ding SX (2013) Model-based fault diagnosis techniques: design schemes, algorithms and tools, 2nd edn. Springer, LondonCrossRefGoogle Scholar
  2. 2.
    Ding SX, Zhang P, Naik A, Ding E, Huang B (2009) Subspace method aided data-driven design of fault detection and isolation systems. J Process Control 19:1496–1510CrossRefGoogle Scholar
  3. 3.
    Mercere G, Bako L (2011) Parameterization and identification of multivariable state-space systems: a canonical approach. Automatica 47:1547–1555CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Wang Y, Ma G, Ding SX, Li C (2011) Subspace aided data-driven design of robust fault detection and isolation systems. Automatica 47:2474–2480CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Wan Y, Ye H (2012) Data-driven diagnosis of sensor precision degradation in the presence of control. J Process Control 22:26–40CrossRefGoogle Scholar
  6. 6.
    Clark RN (1978) Instrument fault detection. IEEE Trans Aerosp Electron Syst 14:456–465Google Scholar
  7. 7.
    Busawon K, Kabore P (2001) Disturbance attenuation using proportional integral observers. Int J Control 74:618–627CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Saif M (1993) Reduced-order proportional integral observer with application. J Guidance Control Dyn 16:985–988CrossRefzbMATHGoogle Scholar
  9. 9.
    Gao Z, Ho D (2004) Proportional multiple-integral observer design for descriptor systems with measurement output disturbances. IEE Proc Control Theory Appl 151(3):279–288CrossRefGoogle Scholar
  10. 10.
    Gao Z, Ho DWC (2006) State/noise estimator for descriptor systems with application to sensor fault diagnosis. IEEE Trans Signal Process 54:1316–1326CrossRefGoogle Scholar
  11. 11.
    Ha QP, Trinh H (2004) State and input simultaneous estimation for a class of nonlinear systems. Automatica 40:1779–1785CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Gao Z, Ding SX (2007) Actuator fault robust estimation and fault-tolerant control for a class of nonlinear descriptor systems. Automatica 43:912–920CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Gao Z, Shi X, Ding S (2008) Fuzzy state/disturbance observer design for T-S fuzzy systems with application to sensor fault estimation. IEEE Trans Syst Man Cybern B Cybern 38:875–880CrossRefGoogle Scholar
  14. 14.
    Yin S, Ding SX, Haghani A, Hao H, Zhang P (2012) A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process. J Process Control 22:1567–1581CrossRefGoogle Scholar
  15. 15.
    Ding SX, Yin S, Peng K, Hao H, Shen B (2013) A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill. IEEE Trans Ind Inform 9:2239–2247Google Scholar
  16. 16.
    Zhou K, Doyle J, Glover K (1996) Robust and optimal control. Prentice-Hall, Upper Saddle RiverzbMATHGoogle Scholar
  17. 17.
    Dong J (2009) Data driven fault tolerant control: a subspace approach. Ph.D. dissertation, Technische Universiteit DelftGoogle Scholar
  18. 18.
    Yu J (2012) A particle filter driven dynamic gaussian mixture model approach for complex process monitoring and fault diagnosis. J Process Control 22:778–788Google Scholar
  19. 19.
    Yu J (2012) Multiway discrete hidden markov model-based approach for dynamic batch process monitoring and fault classification. AIChE J 58:2714–2725CrossRefGoogle Scholar
  20. 20.
    Rashid MM, Yu J (2012) A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-aussian dynamic process monitoring. Chemometr Intell Lab Syst 115:44–58CrossRefGoogle Scholar
  21. 21.
    Yu J (2013) A support vector clustering-based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data. AIChE J 59:407–419CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Institute for Automatic Control and Complex Systems (AKS)University of Duisburg-EssenDuisburgGermany

Personalised recommendations