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Aluminium Process Fault Detection and Diagnosis
Joint Authors
Abd Majid, Nazatul Aini
Taylor, Mark P.
Chen, John J. J.
Young, Brent R.
Source
Advances in Materials Science and Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-15
Country of Publication
Egypt
No. of Pages
11
Abstract EN
The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting.
However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system.
This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation.
Each element is explained together with examples of existing systems.
A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data.
A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments.
American Psychological Association (APA)
Abd Majid, Nazatul Aini& Taylor, Mark P.& Chen, John J. J.& Young, Brent R.. 2015. Aluminium Process Fault Detection and Diagnosis. Advances in Materials Science and Engineering،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1053610
Modern Language Association (MLA)
Abd Majid, Nazatul Aini…[et al.]. Aluminium Process Fault Detection and Diagnosis. Advances in Materials Science and Engineering No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1053610
American Medical Association (AMA)
Abd Majid, Nazatul Aini& Taylor, Mark P.& Chen, John J. J.& Young, Brent R.. Aluminium Process Fault Detection and Diagnosis. Advances in Materials Science and Engineering. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1053610
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1053610