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.
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Sakoe, H., Chiba, S.: Dynamic-Programming Algorithm Optimization for Spoken Word Recognition. IEEE Trans Acoust Speech Signal Process 26, 43–49 (1978)
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)
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)
Vortruba, J., Volesky, B., Yerushalmi, L.: Mathematical model of a batch acetone-butanol fermentation. Biotechnol. Bioeng. 28, 247–255 (1986)
Singhal, A.: Pattern-matching in multivariate time-series data. Ph.D. dissertation, Univ. of California, Santa Barbara (2002)
Luyben William, L.: Process modeling, simulation and control for Chemical Engineers. McGraw Hill, New York (1973)
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)
Kumar, R., Jade, A.M., Jayaraman, V.K., Kulkarni, B.D.: A Hybrid Methodology For On-Line Process Monitoring. IJCREÂ 2(A14) (2004)
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Rajshekhar, Gupta, A., Samanta, A.N., Kulkarni, B.D., Jayaraman, V.K. (2007). Fault Diagnosis Using Dynamic Time Warping. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_8
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DOI: https://doi.org/10.1007/978-3-540-77046-6_8
Publisher Name: Springer, Berlin, Heidelberg
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