Advertisement

Fault Diagnosis Using Dynamic Time Warping

  • Rajshekhar
  • Ankur Gupta
  • A. N. Samanta
  • B. D. Kulkarni
  • V. K. Jayaraman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

Owing to the superiority of Dynamic Time Warping as a similarity measure of time series, it can become an effective tool for fault diagnosis in chemical process plants. However, direct application of Dynamic Time Warping can be computationally inefficient, given the complexity involved. In this work we have tackled this problem by employing a warping window constraint and a Lower Bounding measure. A novel methodology for online fault diagnosis with Dynamic Time Warping has been suggested and its performance has been investigated using two simulated case studies.

Keywords

Fault Diagnosis Dynamic Time Warping Reference Dataset Current Window Reference Window 
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.
    Sakoe, H., Chiba, S.: Dynamic-Programming Algorithm Optimization for Spoken Word Recognition. IEEE Trans Acoust Speech Signal Process 26, 43–49 (1978)zbMATHCrossRefGoogle Scholar
  2. 2.
    Ratanamahatana, C.A., Keogh, E.: Making Time-series Classification More Accurate Using Learned Constraints. In: Jonker, W., Petković, M. (eds.) SDM 2004. LNCS, vol. 3178, pp. 11–22. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Rath, T.M., Manmatha, R.: Lower-Bounding of Dynamic Time Warping Distances for Multivariate Time Series. Technical Report MM-40, Center for Intelligent Information Retrieval, University of Massachusetts Amherst (2002)Google Scholar
  4. 4.
    Vortruba, J., Volesky, B., Yerushalmi, L.: Mathematical model of a batch acetone-butanol fermentation. Biotechnol. Bioeng. 28, 247–255 (1986)CrossRefGoogle Scholar
  5. 5.
    Singhal, A.: Pattern-matching in multivariate time-series data. Ph.D. dissertation, Univ. of California, Santa Barbara (2002)Google Scholar
  6. 6.
    Luyben William, L.: Process modeling, simulation and control for Chemical Engineers. McGraw Hill, New York (1973)Google Scholar
  7. 7.
    Venkatasubramanian, V., Vaidyanathan, R., Yamamoto, Y.: Process fault detection and diagnosis using Neural Networks-I. Steady-state processes. Computers Chem. Engg. 14(7), 699–712 (1990)CrossRefGoogle Scholar
  8. 8.
    Kumar, R., Jade, A.M., Jayaraman, V.K., Kulkarni, B.D.: A Hybrid Methodology For On-Line Process Monitoring. IJCRE 2(A14) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rajshekhar
    • 2
  • Ankur Gupta
    • 2
  • A. N. Samanta
    • 2
  • B. D. Kulkarni
    • 1
  • V. K. Jayaraman
    • 1
  1. 1.Chemical Engineering Division, National Chemical Laboratory, Pune-411008India
  2. 2.Department of Chemical Engineering, Indian Institute of Technology, Kharagpur-721302India

Personalised recommendations