Fault diagnosis using dynamic time warping

被引：0

作者：

Rajshekhar ^{[2
]}

Gupta, Ankur ^{[2
]}

Samanta, A. N. ^{[2
]}

Kulkarni, B. D. ^{[1
]}

Jayaraman, V. K. ^{[1
]}

机构：

[1] Natl Chem Lab, Div Chem Engn, Pune 411008, Maharashtra, India

[2] Indian Inst Technol, Dept Chem Engn, Kharagpur 721302, W Bengal, India

来源：

PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS | 2007年 / 4815卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：57 / +

页数：2

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