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计算机系统应用:2020,29(6):97-103
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基于嵌入式深度学习的电力设备红外热成像故障识别
(1.国网山东省电力公司检修公司, 济南 250100;2.北京科技大学 计算机与通信工程学院, 北京 100081;3.积成电子股份有限公司, 济南 250100;4.中国石油大学(华东) 计算机科学与技术学院, 青岛 266580)
Fault Recognition of Power Equipment in Infrared Thermal Images Based on Deep Learning with Embedded Devices
(1.Overhaul Company, State Grid Shandong Electric Power Company, Jinan 250100, China;2.School of Computer and Communication Engineering, Beijing University of Science and Technology, Beijing 100081, China;3.Jicheng Electronics Co. Ltd., Jinan 250100, China;4.School of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)
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投稿时间:2019-10-19    修订日期:2019-11-15
中文摘要: 随着大型图像集的出现以及计算机硬件尤其是GPU的快速发展, 在有限计算资源的嵌入式设备上部署卷积神经网络(CNN)模型成为具有挑战性的问题. 电力设备过热故障可以通过采集的红外热成像进行识别. 由于红外辐射在空气中传播衰落, 红外测温结果低于实际温度值. 本文提出一种基于嵌入式设备的高效卷积神经网络用于电力设备热故障检测, 将SSD算法中的骨干网络替换为MobileNet, 同时Batch Normalization与前一卷积层合并, 以减少模型参数、提升推理速度、使之能够在轻量级计算平台上运行. 针对红外辐射在空气中传播损失的问题, 提出一种基于BP神经网络的红外测温修正单元. 基于上述创新设计了一种电力设备热故障检测系统, 实验以及现场应用表明, 该方法具有较高的准确性以及推理速度.
Abstract:With the emerging large image sets and the rapid development of computer hardware, especially GPU, the deployment of Convolutional Neural Network (CNN) model on embedded devices with limited computing resources becomes a challenging problem. Overheating of power equipment can be identified from infrared thermal images. Because of the fading of infrared radiation in the air, the result of infrared temperature measurement is lower than the actual value. In this study, an efficient CNN based on embedded devices is proposed for thermal fault detection of power equipment. The backbone network of SSD algorithm is replaced by MobileNet. At the same time, batch normalization is combined with the previous volume to reduce model parameters, improve reasoning speed, and make it run on a lightweight computing platform. To solve the problem of infrared radiation loss in the air, an infrared temperature correction unit based on BP neural network is proposed. Based on the above innovation, a thermal fault detection system for power equipment is designed. Experiments and field applications show that the proposed method has high accuracy and reasoning speed.
文章编号:7422     中图分类号:    文献标志码:
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引用文本:
王彦博,陈培峰,徐亮,张合宝,房凯.基于嵌入式深度学习的电力设备红外热成像故障识别.计算机系统应用,2020,29(6):97-103
WANG Yan-Bo,CHEN Pei-Feng,XU Liang,ZHANG He-Bao,FANG Kai.Fault Recognition of Power Equipment in Infrared Thermal Images Based on Deep Learning with Embedded Devices.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):97-103

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