Metric Learning Method Aided Data-Driven Design of Fault Detection Systems
Joint Authors
Mei, Jiangyuan
Yan, Guoyang
Yin, Shen
Karimi, Hamid Reza
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Fault detection is fundamental to many industrial applications.
With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency.
Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances.
In this paper, we firstly propose a metric learning-based fault detection framework in fault detection.
Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals.
Experiments on Tennessee Eastman (TE) chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA) and fisher discriminate analysis (FDA).
American Psychological Association (APA)
Yan, Guoyang& Mei, Jiangyuan& Yin, Shen& Karimi, Hamid Reza. 2014. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-512793
Modern Language Association (MLA)
Yan, Guoyang…[et al.]. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems. Mathematical Problems in Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-512793
American Medical Association (AMA)
Yan, Guoyang& Mei, Jiangyuan& Yin, Shen& Karimi, Hamid Reza. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-512793
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-512793