Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes

The fault diagnosis approaches based on k -nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k -nearest neighbor rule have been still not sufficiently studied. To tackle thi...

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Autores principales:	Zelin Ren, Yongqiang Tang, Wensheng Zhang
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/34a73138941e459fa6b95d14c2002e13
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spelling	oai:doaj.org-article:34a73138941e459fa6b95d14c2002e132021-12-02T03:05:14ZQuality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes1550-147710.1177/15501477211055931https://doaj.org/article/34a73138941e459fa6b95d14c2002e132021-11-01T00:00:00Zhttps://doi.org/10.1177/15501477211055931https://doaj.org/toc/1550-1477The fault diagnosis approaches based on k -nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k -nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k -nearest neighbor rule, which organically incorporates k -nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k -nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k -nearest neighbor seamlessly, we propose a modified variable contributions by k -nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k -nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k -nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k -nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.Zelin RenYongqiang TangWensheng ZhangSAGE PublishingarticleElectronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Distributed Sensor Networks, Vol 17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Zelin Ren Yongqiang Tang Wensheng Zhang Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
description	The fault diagnosis approaches based on k -nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k -nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k -nearest neighbor rule, which organically incorporates k -nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k -nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k -nearest neighbor seamlessly, we propose a modified variable contributions by k -nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k -nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k -nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k -nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.
format	article
author	Zelin Ren Yongqiang Tang Wensheng Zhang
author_facet	Zelin Ren Yongqiang Tang Wensheng Zhang
author_sort	Zelin Ren
title	Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
title_short	Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
title_full	Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
title_fullStr	Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
title_full_unstemmed	Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
title_sort	quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/34a73138941e459fa6b95d14c2002e13
work_keys_str_mv	AT zelinren qualityrelatedfaultdiagnosisbasedonnearestneighborrulefornonlinearindustrialprocesses AT yongqiangtang qualityrelatedfaultdiagnosisbasedonnearestneighborrulefornonlinearindustrialprocesses AT wenshengzhang qualityrelatedfaultdiagnosisbasedonnearestneighborrulefornonlinearindustrialprocesses
_version_	1718401974732324864

Quality-related fault diagnosis based on -nearest neighbor rule for non-linear industrial processes

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