Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric c...

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Autores principales:	Jintao Meng, Hao Zhang, Xue Wang, Yue Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	atmospheric corrosion carbon steel data mining environmental factor evidence theory Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/656279e9909f4e09bc8cf55ea292ebf7
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id	oai:doaj.org-article:656279e9909f4e09bc8cf55ea292ebf7
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spelling	oai:doaj.org-article:656279e9909f4e09bc8cf55ea292ebf72021-11-25T18:14:59ZData Mining to Atmospheric Corrosion Process Based on Evidence Fusion10.3390/ma142269541996-1944https://doaj.org/article/656279e9909f4e09bc8cf55ea292ebf72021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/22/6954https://doaj.org/toc/1996-1944An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.Jintao MengHao ZhangXue WangYue ZhaoMDPI AGarticleatmospheric corrosioncarbon steeldata miningenvironmental factorevidence theoryTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6954, p 6954 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	atmospheric corrosion carbon steel data mining environmental factor evidence theory Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	atmospheric corrosion carbon steel data mining environmental factor evidence theory Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Jintao Meng Hao Zhang Xue Wang Yue Zhao Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion
description	An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.
format	article
author	Jintao Meng Hao Zhang Xue Wang Yue Zhao
author_facet	Jintao Meng Hao Zhang Xue Wang Yue Zhao
author_sort	Jintao Meng
title	Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion
title_short	Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion
title_full	Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion
title_fullStr	Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion
title_full_unstemmed	Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion
title_sort	data mining to atmospheric corrosion process based on evidence fusion
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/656279e9909f4e09bc8cf55ea292ebf7
work_keys_str_mv	AT jintaomeng dataminingtoatmosphericcorrosionprocessbasedonevidencefusion AT haozhang dataminingtoatmosphericcorrosionprocessbasedonevidencefusion AT xuewang dataminingtoatmosphericcorrosionprocessbasedonevidencefusion AT yuezhao dataminingtoatmosphericcorrosionprocessbasedonevidencefusion
_version_	1718411440731193344

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

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