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Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems pp 95–116Cite as

Application of Partial Least Squares Regression to Fault Diagnosis

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Part of the book series: Advances in Industrial Control ((AIC))

Abstract

Roughly speaking, partial least squares (PLS) regression is a multivariate analysis method that constructs a linear regression between two data sets expressed in form of data matrices.

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References

  1. Chiang LH, Russell EL, Braatz RD (2001) Fault detection and diagnosis in industrial systems. Springer, London

    Google Scholar 

  2. Dayal B, MacGregor JF (1997) Improved PLS algorithms. J Chemometr 11:73–85

    Article  Google Scholar 

  3. Helland IS (1988) On the structure of partial least squares regression. Commun stat simul comput 17:581–607

    Article  MATH  MathSciNet  Google Scholar 

  4. Höskuldsson A (1988) PLS regression methods. J Chemomet 2:211–228

    Article  Google Scholar 

  5. Kresta J, MacGregor J, Marlin T (1991) Multivariate statistical monitoring of process operating performance. Canadian J Chem Eng 69:35–47

    Article  Google Scholar 

  6. Li G, Qin SJ, Zhou D (2010) Geometric properties of partial least squares for process monitoring. Automatica 46(1):204–210

    Google Scholar 

  7. Li G, Alcala CF, Qin SJ, Zhou D (2011) Generalized reconstruction-baed contributions for output-relevant fault diagnosis with application to the tennessee eastman process. IEEE Trans Contr Syst Tech 19:1114–1127

    Article  Google Scholar 

  8. MacGregor JF, Kourti T (1995) Statistical process control of multivariate processes. Contr Eng Practice 3:403–414

    Article  Google Scholar 

  9. Peng KX, Zhang K, Li G, Zhou D (2013) Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process. Contr Eng Practice 21:360–369

    Article  Google Scholar 

  10. Qin SJ (1998) Recursive PLS algorithms for adaptive data modeling. Computers Chem Eng 22:503–514

    Article  Google Scholar 

  11. Wise B, Gallagher N (1996) The process chemometrics approach to process monitoring and fault detection. J Process Control 6:329–348

    Article  Google Scholar 

  12. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemomet Int Lab Syst 58:109–130

    Article  Google Scholar 

  13. 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 22:1567–1581

    Article  Google Scholar 

  14. Zhou D, Li G, Qin SJ (2010) Total projection to latent structures for process monitoring. AIChE J 56:168–178

    Google Scholar 

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Authors and Affiliations

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

    Steven X. Ding

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  1. Steven X. Ding
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Correspondence to Steven X. Ding .

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© 2014 Springer-Verlag London

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Ding, S.X. (2014). Application of Partial Least Squares Regression to Fault Diagnosis. In: Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-6410-4_6

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  • DOI: https://doi.org/10.1007/978-1-4471-6410-4_6

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6409-8

  • Online ISBN: 978-1-4471-6410-4

  • eBook Packages: EngineeringEngineering (R0)

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