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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Rajshekhar, Gupta, A., Samanta, A. N., Kulkarni, B. D., & Jayaraman, V. K. (2007). Fault diagnosis using dynamic time warping. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4815 LNCS, pp. 57–66). Springer Verlag. https://doi.org/10.1007/978-3-540-77046-6_8
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