首页|基于热红外图像的船舶电气设备状态异常检测研究

基于热红外图像的船舶电气设备状态异常检测研究

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可靠掌握电气设备的运行状态,是保证船舶安全航行的基础.因此,提出基于热红外图像的船舶电气设备状态异常检测方法.该方法依据红外成像技术获取船舶电气设备成像,获取其热红外图像结果,并计算电气设备温度概率密度函数,以此描述电气设备的温度分布特征.将该概率密度函数计算结果输入具备增量学习的宽度学习算法中,完成船舶电气设备不同异常状态检测.测试结果显示,将温度概率密度作为电气设备状态异常检测依据,能够更好地区分电气设备的正常放热以及故障升温;AUC的测试结果均在0.94以上,可确定电气设备运行过程中的不同程度异常状态.
Research on abnormal detection of ship electrical equipment status based on thermal infrared images
Reliable understanding of the operating status of electrical equipment is the foundation for ensuring safe nav-igation of ships.Therefore,a method for detecting abnormal status of ship electrical equipment based on thermal infrared im-ages is proposed.This method obtains imaging of ship electrical equipment based on infrared imaging technology and ob-tains 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 equip-ment.The test results of AUC are all above 0.94,identify varying degrees of abnormal conditions during the operation of electrical equipment.

thermal infrared imagingship electrical equipmentabnormal state detectionprobability density func-tiontemperature distribution characteristicswidth learning

崔海花、赵英凯

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河南科技职业大学机电工程学院,河南周口 466000

热红外图像 船舶电气设备 状态异常检测 概率密度函数 温度分布特征 宽度学习

河南省重点研发与推广专项(科技攻关)项目

222102210090

2024

舰船科学技术
中国舰船研究院,中国船舶信息中心

舰船科学技术

CSTPCD北大核心
影响因子:0.373
ISSN:1672-7649
年,卷(期):2024.46(3)
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