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作 者:侯晋阳 沈庆华 孙磊 王伟光 李虹霞 HOU Jinyang;SHEN Qinghua;SUN Lei;WANG Weiguang;LI Hongxia(Datang Tashan Coal Mine Co.,Ltd.,Datong Coal Mine Group,Datong 037001,Shanxi,China)
机构地区:[1]同煤大唐塔山煤矿有限公司,山西大同037001
出 处:《能源与节能》2025年第4期78-80,共3页Energy and Energy Conservation
摘 要:在无人值守煤矿变电所场景下,基于巡检机器人终端,设计了图像捕捉、红外测温、基于RBF神经网络的故障识别功能。经仿真和实验验证,相比BP神经网络,RBF神经网络的异常识别精确度达到95%,红外测温误差控制在4%以内,每月停电检修时间缩减了8 h,验证设计可行。In the context of unattended substations in coal mines,an inspection robot terminal was designed with functions including image capture,infrared temperature measurement,and fault identification based on RBF neural networks.Simulation and experimental verification showed that compared with BP neural network,the accuracy of anomaly identification using the RBF neural network reached 95%,the measurement error of infrared temperature measurement was controlled within 4%,and the power outage maintenance time was reduced by 8 h per month,confirming the feasibility of the design.
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