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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Materials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1944/14/22/6954 |
| _version_ | 1797509509374017536 |
|---|---|
| author | Jintao Meng Hao Zhang Xue Wang Yue Zhao |
| author_facet | Jintao Meng Hao Zhang Xue Wang Yue Zhao |
| author_sort | Jintao Meng |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:18:46Z |
| format | Article |
| id | doaj.art-656279e9909f4e09bc8cf55ea292ebf7 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T05:18:46Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-656279e9909f4e09bc8cf55ea292ebf72023-11-23T00:11:23ZengMDPI AGMaterials1996-19442021-11-011422695410.3390/ma14226954Data Mining to Atmospheric Corrosion Process Based on Evidence FusionJintao Meng0Hao Zhang1Xue Wang2Yue Zhao3Science and Technology on Communication Security Laboratory, Chengdu 610041, ChinaScience and Technology on Communication Security Laboratory, Chengdu 610041, ChinaScience and Technology on Communication Security Laboratory, Chengdu 610041, ChinaScience and Technology on Communication Security Laboratory, Chengdu 610041, ChinaAn 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.https://www.mdpi.com/1996-1944/14/22/6954atmospheric corrosioncarbon steeldata miningenvironmental factorevidence theory |
| spellingShingle | Jintao Meng Hao Zhang Xue Wang Yue Zhao Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion Materials atmospheric corrosion carbon steel data mining environmental factor evidence theory |
| title | 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_short | Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion |
| title_sort | data mining to atmospheric corrosion process based on evidence fusion |
| topic | atmospheric corrosion carbon steel data mining environmental factor evidence theory |
| url | https://www.mdpi.com/1996-1944/14/22/6954 |
| work_keys_str_mv | AT jintaomeng dataminingtoatmosphericcorrosionprocessbasedonevidencefusion AT haozhang dataminingtoatmosphericcorrosionprocessbasedonevidencefusion AT xuewang dataminingtoatmosphericcorrosionprocessbasedonevidencefusion AT yuezhao dataminingtoatmosphericcorrosionprocessbasedonevidencefusion |