A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure
Joint Authors
Mei, Jiangyuan
Hou, Jian
Huang, Jiarao
Karimi, Hamid Reza
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-15
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Mei, Jiangyuan& Hou, Jian& Karimi, Hamid Reza& Huang, Jiarao. 2014. A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1033883
Modern Language Association (MLA)
Mei, Jiangyuan…[et al.]. A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure. Abstract and Applied Analysis No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1033883
American Medical Association (AMA)
Mei, Jiangyuan& Hou, Jian& Karimi, Hamid Reza& Huang, Jiarao. A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1033883
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033883