Advertisement

Adaptive Fault Diagnosis Schemes

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

Abstract

The key of data-driven fault diagnosis schemes consists in a direct identification of key fault detection statistics like covariance matrices applied in the MVA or SIM based methods.

Keywords

Fault Detection Singular Vector Adaptive Observer Residual Generator Fault Detection Scheme 
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.

References

  1. 1.
    Helland DM, Bernstein HE, Borgen O, Martens H (1992) Recursive algorithm for partial least squares regression. Chemometr Intell Lab Syst 14:129–137CrossRefGoogle Scholar
  2. 2.
    Qin SJ (1998) Recursive PLS algorithms for adaptive data modeling. Comput Chem Eng 22:503–514CrossRefGoogle Scholar
  3. 3.
    Li W, Yue HH, Valle-Cervantes S, Qin SJ (2000) Recursive PCA for adaptive process monitoring. J Process Control 10:471–486CrossRefGoogle Scholar
  4. 4.
    Elshenawy LM, Yin S, Naik AS, Ding SX (2010) Efficient recursive principal component analysis algorithms for proces monitoring. Ind Eng Chem Res 49:252–259CrossRefGoogle Scholar
  5. 5.
    Golub GH, Loan CFV (1993) Matrix computations, 2nd edn. The John Hopkins University Press, BaltimoreGoogle Scholar
  6. 6.
    Doukopoulos XG, Moustakides GV (2008) Fast and stable subspace tracking. IEEE Trans Sig Process 56:1452–1465CrossRefMathSciNetGoogle Scholar
  7. 7.
    Stewart GW, Sun J-G (1990) Matrix perturbation theory. Academic Press, San DiegoMATHGoogle Scholar
  8. 8.
    Willink TJ (2008) Efficient adaptive SVD algorithm for MIMO applications. IEEE Trans Sig Process 56:615–622CrossRefMathSciNetGoogle Scholar
  9. 9.
    Naik AS, Yin S, Ding SX, Zhang P (2010) Recursive identification algorithms to design fault detection systems. J Process Control 20:957–965CrossRefGoogle Scholar
  10. 10.
    Wang X, Kruger U, Irwin GW (2005) Process monitoring approach using fast moving window PCA. Ind Eng Chem Res 44:5691–5702Google Scholar
  11. 11.
    He XB, Yang YP (2008) Variable MWPCA for adaptive process monitoring. Ind Eng Chem Res 47:419–427Google Scholar
  12. 12.
    Zhang Q (2002) Adaptive observer for multiple-input-multiple-output (MIMO) linear time-varying systems. IEEE Trans Autom Control 47:525–529CrossRefGoogle Scholar
  13. 13.
    Caccavale F, Pierri F, Villani L (2008) Adaptive observer for fault aidgnosis in nonlinear discrete-time systems. J Dyn Syst Meas Control 130:021 005–1–021 005–9Google Scholar
  14. 14.
    Aström KJ, Wittenmark B (1995) Adaptive control. Addison-Wesley Publishing Company, ReadingMATHGoogle Scholar
  15. 15.
    Bastin G, Gevers M (1988) Stable adaptive observers for nonlinear time-varying systems. IEEE Trans Autom Control 33:650–658CrossRefMATHMathSciNetGoogle 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