﻿ 基于热红外图像的船舶电气设备状态异常检测研究
 舰船科学技术  2024, Vol. 46 Issue (3): 147-150    DOI: 10.3404/j.issn.1672-7649.2024.03.026 PDF

Research on abnormal detection of ship electrical equipment status based on thermal infrared images
CUI Hai-hua, ZHAO Ying-Kai
School of Mechano-Electronic Engineering, Henan Vocational University of Science and Technology, Zhoukou 466000, China
Abstract: Reliable understanding of the operating status of electrical equipment is the foundation for ensuring safe navigation of ships. Therefore, a method for detecting abnormal status of ship electrical equipment based on thermal infrared images is proposed. This method obtains imaging of ship electrical equipment based on infrared imaging technology and obtains its thermal infrared image results, and calculate the probability density function of electrical equipment temperature to describe the temperature distribution characteristics of electrical equipment; Input the calculated result of the probability density function into a width learning algorithm with incremental learning to complete the detection of different abnormal states of ship electrical equipment. The test results show that using temperature probability density as the basis for detecting abnormal electrical equipment status can better distinguish between normal heat release and fault heating of electrical equipment. The test results of AUC are all above 0.94, identify varying degrees of abnormal conditions during the operation of electrical equipment.
Key words: thermal infrared imaging     ship electrical equipment     abnormal state detection     probability density function     temperature distribution characteristics     width learning
0 引　言

1 基于热红外图像的船舶电气设备状态异常检方法 1.1 红外热成像原理

 图 1 红外热成像原理示意图 Fig. 1 Schematic diagram of infrared thermal imaging principle
1.2 电气设备温度概率密度函数计算

 $f\left( x \right) = \frac{{{N_x}}}{{{N_{sum}}}},x \in \left( {{\theta _{\min }},{\theta _{\max }}} \right) 。$ (1)

 $Y = \left[ {{Z_n}\left| {{H_m}} \right.} \right]{W_m}。$ (7)

 ${W_m} = {\left[ {{Z_n}\left| {{H_m}} \right.} \right]^ + }Y 。$ (8)

 ${}_{{Y_a}}{W_m} = {\left[ {{Z_n}\left| {{H_m}} \right.} \right]^ + }\left[ {\begin{array}{*{20}{c}} {Y,0} \\ {{Y_a}} \end{array}} \right] 。$ (9)

 $\left[ {\begin{array}{*{20}{c}} {{b_1}} \\ {{b_2}} \\ {{b_3}} \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} 1&0&0 \\ 0&1&0 \\ 0&0&1 \end{array}} \right]。$ (10)

 $\left[ {\begin{array}{*{20}{c}} {{b_1}} \\ {{b_2}} \\ {{b_3}} \\ {{b_4}} \\ {{b_5}} \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} 1&0&0&0&0 \\ 0&1&0&0&0 \\ 0&0&1&0&0 \\ 0&0&0&1&0 \\ 0&0&0&0&1 \end{array}} \right] 。$ (11)

2 性能测试与分析

 图 2 电气设备热红外图像的温度概率密度计算结果 Fig. 2 Calculation results of temperature probability density for thermal infrared images of electrical equipment

 $AUC = \int_{ - \infty }^{ + \infty } {\gamma \left( T \right)\mu \left( T \right){\rm{d}}T} 。$ (12)

3 结　语

 [1] 赵颖祺, 陈玉虎, 张晰, 等. 一种基于箱线图的SAR图像船舶检测算法研究[J]. 中国海洋大学学报(自然科学版), 2021, 51(10): 130-140. [2] 曾军, 王东杰, 范伟, 等. 基于红外热成像的电气设备组件识别研究[J]. 红外技术, 2021, 43(7): 679-687. [3] 宋世静. 基于数据挖掘的电气设备状态自动检测方法[J]. 自动化技术与应用, 2023, 42(8): 133-136. [4] 律方成, 牛雷雷, 王胜辉, 等. 基于优化YOLOv4的主要电气设备智能检测及调参策略[J]. 电工技术学报, 2021, 36(22): 4837-4848. [5] 翟洪婷, 张庆锐, 卞若晨, 等. 基于图聚类的电力设备异常声音检测方法[J]. 南京理工大学学报, 2022, 46(3): 270-276. [6] 党晓婧, 刘顺桂, 朱光南, 等. 基于特征提取的电气设备红外图像识别算法[J]. 沈阳工业大学学报, 2023, 45(3): 264-269. [7] 霍成军, 史奕龙, 武晓磊, 等. 基于热成像技术的电气设备目标检测方法[J]. 激光与红外, 2021, 51(4): 530-536.