Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

被引:29
作者
Imran, Md Mahadi Hasan [1 ]
Jamaludin, Shahrizan [1 ]
Ayob, Ahmad Faisal Mohamad [1 ]
Ali, Ahmad Ali Imran Mohd [1 ]
Ahmad, Sayyid Zainal Abidin Syed [1 ]
Akhbar, Mohd Faizal Ali [1 ]
Suhrab, Mohammed Ismail Russtam [2 ]
Zainal, Nasharuddin [3 ]
Norzeli, Syamimi Mohd [4 ]
Mohamed, Saiful Bahri [4 ]
机构
[1] Univ Malaysia Terengganu, Fac Ocean Engn Technol & Informat, Terengganu 21030, Malaysia
[2] Univ Malaysia Terengganu, Fac Maritime Studies, Terengganu 21030, Malaysia
[3] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Elect Elect & Syst Engn, Bangi 43600, Selangor, Malaysia
[4] Univ Sultan Zainal Abidin, Fac Innovat Design & Technol, Terengganu 21030, Malaysia
关键词
corrosion prediction; corrosion detection; predictive maintenance; computer vision; image processing; USEFUL LIFE ESTIMATION; SUB-IRIS TECHNIQUE; ACOUSTIC-EMISSION; PITTING CORROSION; ELECTROCHEMICAL NOISE; AUTOMATED DETECTION; SPEECH RECOGNITION; NEURAL-NETWORKS; CO2; CORROSION; STEEL;
D O I
10.3390/jmse11020256
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.
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页数:25
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