Mining Methods for Material Corrosion Data

QIN Yanmin; LI Xingeng; AN Jiangfeng; FAN Zhibin; WU Jun; WANG Xiaoming

doi:10.11973/fsyfh-202304012

Corrosion & Protection > 2023 > 44(4): 72-81. > DOI: 10.11973/fsyfh-202304012

QIN Yanmin, LI Xingeng, AN Jiangfeng, FAN Zhibin, WU Jun, WANG Xiaoming. Mining Methods for Material Corrosion Data[J]. Corrosion & Protection, 2023, 44(4): 72-81. DOI: 10.11973/fsyfh-202304012

Citation:

QIN Yanmin, LI Xingeng, AN Jiangfeng, FAN Zhibin, WU Jun, WANG Xiaoming. Mining Methods for Material Corrosion Data[J]. Corrosion & Protection, 2023, 44(4): 72-81. DOI: 10.11973/fsyfh-202304012

Citation:

QIN Yanmin, LI Xingeng, AN Jiangfeng, FAN Zhibin, WU Jun, WANG Xiaoming. Mining Methods for Material Corrosion Data[J]. Corrosion & Protection, 2023, 44(4): 72-81. DOI: 10.11973/fsyfh-202304012

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Mining Methods for Material Corrosion Data

1. Wuhan Research Institute of Materials Protection, Wuhan 430030, China;
2. State Grid Shandong Electric Power Research Institute, Jinan 250003, China;
3. Wuhan Materials Corrosion National Observation and Research Station, Wuhan 430030, China

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Received Date: March 11, 2022

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Abstract

Abstract

The methods of material corrosion data mining used in historical documents are summarized. It includes the independent or integrated application of curve fitting, multiple linear regression analysis, grey theory, neural network, decision regression tree and cluster analysis. The existing problems of corrosion data analysis are pointed out, and the development trend of material corrosion data mining technology is prospected.
- material corrosion,
- data mining,
- current problem,
- development trend

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References (81)

References

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