Aluminium Process Fault Detection and Diagnosis
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 al...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/682786 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568112191373312 |
|---|---|
| author | Nazatul Aini Abd Majid Mark P. Taylor John J. J. Chen Brent R. Young |
| author_facet | Nazatul Aini Abd Majid Mark P. Taylor John J. J. Chen Brent R. Young |
| author_sort | Nazatul Aini Abd Majid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e414e43147974a558645524fc4b632cf |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-e414e43147974a558645524fc4b632cf2025-02-03T00:59:44ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422015-01-01201510.1155/2015/682786682786Aluminium Process Fault Detection and DiagnosisNazatul Aini Abd Majid0Mark P. Taylor1John J. J. Chen2Brent R. Young3Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Selangor, MalaysiaDepartment of Chemical and Materials Engineering, The University of Auckland, Auckland 1142, New ZealandDepartment of Chemical and Materials Engineering, The University of Auckland, Auckland 1142, New ZealandDepartment of Chemical and Materials Engineering, The University of Auckland, Auckland 1142, New ZealandThe 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.http://dx.doi.org/10.1155/2015/682786 |
| spellingShingle | Nazatul Aini Abd Majid Mark P. Taylor John J. J. Chen Brent R. Young Aluminium Process Fault Detection and Diagnosis Advances in Materials Science and Engineering |
| title | Aluminium Process Fault Detection and Diagnosis |
| title_full | Aluminium Process Fault Detection and Diagnosis |
| title_fullStr | Aluminium Process Fault Detection and Diagnosis |
| title_full_unstemmed | Aluminium Process Fault Detection and Diagnosis |
| title_short | Aluminium Process Fault Detection and Diagnosis |
| title_sort | aluminium process fault detection and diagnosis |
| url | http://dx.doi.org/10.1155/2015/682786 |
| work_keys_str_mv | AT nazatulainiabdmajid aluminiumprocessfaultdetectionanddiagnosis AT markptaylor aluminiumprocessfaultdetectionanddiagnosis AT johnjjchen aluminiumprocessfaultdetectionanddiagnosis AT brentryoung aluminiumprocessfaultdetectionanddiagnosis |