@conference { ISI:000252140200008,
	title = {Fault diagnosis using dynamic time warping},
	booktitle = {Pattern Recognition and Machine Intelligence, Proceedings},
	series = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {4815},
	year = {2007},
	note = {2nd International Conference on Pattern Recognition and Machine Intelligence, Calcutta, INDIA, DEC 18-22, 2007},
	month = {DEC},
	pages = {57-66},
	publisher = {Indian Stat Inst, Machine Intelligence Univ; ISI Ctr Soft Comp Res; Int Assoc Pattern Recognit; Int Ctr Pure \& Appl Math; Web Intelligence Consortium; Yahoo India Res \& Dev; Philips Res Asia},
	organization = {Indian Stat Inst, Machine Intelligence Univ; ISI Ctr Soft Comp Res; Int Assoc Pattern Recognit; Int Ctr Pure \& Appl Math; Web Intelligence Consortium; Yahoo India Res \& Dev; Philips Res Asia},
	address = {Heidelberger Platz 3, D-14197 Berlin, Germany},
	abstract = {<p>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.</p>},
	isbn = {978-3-540-77045-9},
	issn = {0302-9743},
	author = {Rajshekhar and Gupta, Ankur and Samanta, A. N. and Kulkarni, B. D. and Jayaraman, Valadi K.},
	editor = {Ghosh, A. and De, R. K. and Pal, S. K.}
}
