dc.contributor.author |
Brooks, Kevin S.
|
|
dc.contributor.author |
Bauer, Margret
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|
dc.date.accessioned |
2018-06-20T09:27:16Z |
|
dc.date.issued |
2018-08 |
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dc.description.abstract |
Detecting the failure of a sensor in industrial processes is important to avoid the use of incorrect measurements. When a sensor fails the missing measurement are reconstructed, using the measurements of other sensors and inferring the missing or incorrect measurement. Although this technology has been developed more than 20 years ago, there are few commercial solutions available today. One of these few solutions uses principal component analysis, based on an algorithm originally developed by Qin and Li (1999). In this paper, this solution is applied to operating data from a minerals processing plant with persistent sensor problems Somewhat surprisingly, poor results are obtained, despite numerous attempts to improve reconstructability. Analysis indicates that the challenges are not about the algorithm but rather about choices that need to be made in the application of data-driven analysis tools to new data sets. These include data selection, filtering and interpreting which results are useful. It is suggested that together with any new algorithm presented. researchers should provide practical guidelines in choosing appropriate data, and any pre-processing that may be required. |
en_ZA |
dc.description.department |
Electrical, Electronic and Computer Engineering |
en_ZA |
dc.description.embargo |
2019-08-01 |
|
dc.description.librarian |
hj2018 |
en_ZA |
dc.description.uri |
http://www.elsevier.com/locate/conengprac |
en_ZA |
dc.identifier.citation |
Brooks, K.S. & Bauer, M. 2018, 'Sensor validation and reconstruction : experiences with commercial technology', Control Engineering Practice, vol. 77, pp. 28-40. |
en_ZA |
dc.identifier.issn |
0967-0661 (print) |
|
dc.identifier.issn |
1873-6939 (online) |
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dc.identifier.other |
10.1016/j.conengprac.2018.04.003 |
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dc.identifier.uri |
http://hdl.handle.net/2263/65181 |
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dc.language.iso |
en |
en_ZA |
dc.publisher |
Elsevier |
en_ZA |
dc.rights |
© 2018 Published by Elsevier Ltd. Notice : this is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Control Engineering Practice, vol. 77, pp. 28-40, 2018. doi : 10.1016/j.conengprac.2018.04.003. |
en_ZA |
dc.subject |
Process control |
en_ZA |
dc.subject |
Fault detection |
en_ZA |
dc.subject |
Fault diagnosis |
en_ZA |
dc.subject |
Principal component analysis |
en_ZA |
dc.subject |
Commercial software tools |
en_ZA |
dc.subject |
Sensor reconstruction |
en_ZA |
dc.subject |
Sensor validation |
en_ZA |
dc.subject |
Practical guidelines |
en_ZA |
dc.subject |
Missing measurements |
en_ZA |
dc.subject |
Minerals processing plants |
en_ZA |
dc.subject |
Incorrect measurements |
en_ZA |
dc.subject |
Commercial technology |
en_ZA |
dc.subject |
Failure analysis |
en_ZA |
dc.subject |
Data handling |
en_ZA |
dc.title |
Sensor validation and reconstruction : experiences with commercial technology |
en_ZA |
dc.type |
Postprint Article |
en_ZA |