Abstract and Applied Analysis

A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure

Jiangyuan Mei, Jian Hou, Hamid Reza Karimi, and Jiarao Huang

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Abstract

Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric learning, MTS pieces building, and MTS classification is presented. After that, we conduct experiments on industrial benchmark of Tennessee Eastman (TE) process. The experimental results demonstrate the improved performance of the proposed algorithm when compared with other classical PM-FD classical methods.

Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 625814, 8 pages.

Dates
First available in Project Euclid: 6 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412606637

Digital Object Identifier
doi:10.1155/2014/625814

Zentralblatt MATH identifier
07022760

Citation

Mei, Jiangyuan; Hou, Jian; Karimi, Hamid Reza; Huang, Jiarao. A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure. Abstr. Appl. Anal. 2014 (2014), Article ID 625814, 8 pages. doi:10.1155/2014/625814. https://projecteuclid.org/euclid.aaa/1412606637


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