腐蚀电场对舰船涂层的破损位置检测
2025,47(2):227-238
胡育诚
海军工程大学 电气工程学院, 湖北 武汉 430033
王向军
海军工程大学 电气工程学院, 湖北 武汉 430033
刘武强
海军工程大学 电气工程学院, 湖北 武汉 430033
汪石川
海军工程大学 电气工程学院, 湖北 武汉 430033
柳懿
海军工程大学 电气工程学院, 湖北 武汉 430033
海军工程大学 电气工程学院, 湖北 武汉 430033
王向军
海军工程大学 电气工程学院, 湖北 武汉 430033
刘武强
海军工程大学 电气工程学院, 湖北 武汉 430033
汪石川
海军工程大学 电气工程学院, 湖北 武汉 430033
柳懿
海军工程大学 电气工程学院, 湖北 武汉 430033
摘要：
舰船腐蚀电场信号具有频率低、难以消除等特性，是一种线谱特征明显的船舶物理场特征。不同涂层破损区域的舰船具有区分明显的电场分布特性，可以利用腐蚀电场信号实现舰船的涂层破损位置检测。为此，提出一种结合精细复合层次反向波动色散熵(refined composite hierarchical fluctuation revise dispersion entropy, RCHFRDE)和改进哈里斯鹰优化核极限学习机(improved Harris Hawk optimization-kernel based extreme learning machine, IHHO-KELM)的检测方法。使用RCHFRDE提取腐蚀电场信号的特征信息，输入IHHO-KELM进行训练检测涂层损伤区域。通过仿真实验和缩比船模实验来验证所提方法的有效性与可靠性。实验结果证明，该方法能有效预测舰船涂层的单个破损区域，仿真数据和测量数据的检测准确率分别达到94-67%和89-00%，在先验环境信息较少的情况下可以作为非接触式检测方法的有效补充。
舰船腐蚀电场信号具有频率低、难以消除等特性，是一种线谱特征明显的船舶物理场特征。不同涂层破损区域的舰船具有区分明显的电场分布特性，可以利用腐蚀电场信号实现舰船的涂层破损位置检测。为此，提出一种结合精细复合层次反向波动色散熵(refined composite hierarchical fluctuation revise dispersion entropy, RCHFRDE)和改进哈里斯鹰优化核极限学习机(improved Harris Hawk optimization-kernel based extreme learning machine, IHHO-KELM)的检测方法。使用RCHFRDE提取腐蚀电场信号的特征信息，输入IHHO-KELM进行训练检测涂层损伤区域。通过仿真实验和缩比船模实验来验证所提方法的有效性与可靠性。实验结果证明，该方法能有效预测舰船涂层的单个破损区域，仿真数据和测量数据的检测准确率分别达到94-67%和89-00%，在先验环境信息较少的情况下可以作为非接触式检测方法的有效补充。
基金项目：
国家自然科学基金资助项目(41476153)
国家自然科学基金资助项目(41476153)
Coating damage detection of vessels using corrosion electric field
HU Yucheng
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
WANG Xiangjun
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
LIU Wuqiang
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
WANG Shichuan
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
LIU Yi
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
WANG Xiangjun
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
LIU Wuqiang
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
WANG Shichuan
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
LIU Yi
College of Electrical Engineering, Naval University of Engineering, Wuhan 430033 , China
Abstract：
The corrosion electric field signal of ships has characteristics such as low frequency and difficulty in elimination, and it is a kind of physical field feature of ships with obvious line spectrum characteristics. Ships with different coating damage areas have distinct electric field distribution characteristics, and the corrosion electric field signal can be utilized to detect the coating damage location of ships. Therefore, a detection method combining RCHFRDE(refined composite hierarchical fluctuation revise dispersion entropy) and IHHO-KELM(improved Harris Hawk optimization-kernel based extreme learning machine) was proposed. RCHFRDE was used to extract the feature information of the corrosion electric field signal, which was then input into IHHO-KELM for training to detect the coating damage area. The effectiveness and reliability of the proposed method were verified through simulation experiments and scale model experiments of ships. The experimental results show that this method can effectively predict the single damage area of the ships coating. The detection accuracy rates of simulation data and measurement data reach 94-67% and 89-00% respectively. It can be used as an effective supplement to non-contact detection methods in cases with less prior environmental information.
The corrosion electric field signal of ships has characteristics such as low frequency and difficulty in elimination, and it is a kind of physical field feature of ships with obvious line spectrum characteristics. Ships with different coating damage areas have distinct electric field distribution characteristics, and the corrosion electric field signal can be utilized to detect the coating damage location of ships. Therefore, a detection method combining RCHFRDE(refined composite hierarchical fluctuation revise dispersion entropy) and IHHO-KELM(improved Harris Hawk optimization-kernel based extreme learning machine) was proposed. RCHFRDE was used to extract the feature information of the corrosion electric field signal, which was then input into IHHO-KELM for training to detect the coating damage area. The effectiveness and reliability of the proposed method were verified through simulation experiments and scale model experiments of ships. The experimental results show that this method can effectively predict the single damage area of the ships coating. The detection accuracy rates of simulation data and measurement data reach 94-67% and 89-00% respectively. It can be used as an effective supplement to non-contact detection methods in cases with less prior environmental information.
收稿日期：
2022-11-07
2022-11-07
