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| Research Progress on Corrosion Prediction Model of Metallic Materials for Electrical Equipment |
YAO Yong1, LIU Guojun1, LI Shizhu1, LIU Miaoran2( ), CHEN Chuan2, HUANG Tingcheng2, LIN Hai3, LI Zhanjiang3, LIU Yuwei4, WANG Zhenyao4 |
1.Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China
2.China National Electric Apparatus Research Institute Co., Ltd., Guangzhou 510799, China
3.Zhanjiang Customs Technology Center, Zhanjiang 524000, China
4.Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China |
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Abstract Metallic materials for electrical equipment are affected by many factors related with environment during service, and their corrosion behavior is very complex, therefore, which is difficult to be accurately predicted. The development of computer technology and data analysis technology enriches the prediction methods for corrosion behavior of metallic materials with better accuracy. This paper summarizes and analyzes the existing common corrosion prediction methods in the field of corrosion, including function model, grey theory model, neural network prediction model, dose response function model and random forest model etc., and which then are classified into two types, namely corrosion-time models and corrosion-environment prediction models. Furthermore, the characteristics and application scope of different corrosion prediction models are introduced. Finally, prospects for the corrosion prediction of metallic materials are put forward especially in terms of the demands of power industry.
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Received: 25 October 2022
32134.14.1005.4537.2022.332
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| Fund: Science and Technology Project of Guangdong Energy Group Co., Ltd(STI-PY-21009) |
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Corresponding Authors:
LIU Miaoran, E-mail: liumr@cei1958.com
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Cite this article:
YAO Yong, LIU Guojun, LI Shizhu, LIU Miaoran, CHEN Chuan, HUANG Tingcheng, LIN Hai, LI Zhanjiang, LIU Yuwei, WANG Zhenyao. Research Progress on Corrosion Prediction Model of Metallic Materials for Electrical Equipment. Journal of Chinese Society for Corrosion and protection, 2023, 43(5): 983-991.
URL:
https://www.jcscp.org/EN/10.11902/1005.4537.2022.332 OR https://www.jcscp.org/EN/Y2023/V43/I5/983
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