电子测量与仪器学报

孙绍珩,鲁彩江,曹中清,刘子轩,江雪玲,李林峰.基于深度学习的杆塔接地网断点诊断方法研究[J].电子测量与仪器学报,2021,35(10):168-175

基于深度学习的杆塔接地网断点诊断方法研究

Research on diagnosis method of tower grounding gridbreakpoints based on deep learning

DOI：

英文关键词:electromagnetic induction method tower grounding grid convolutional neural network breakpoint fault diagnosis

基金项目:国家自然科学基金（61801402）、四川省杰出青年科技人才项目（2020JDJQ0038)、中央高校基本科研业务费（2682020CX26）项目资助

作者	单位
孙绍珩	1. 西南交通大学机械工程学院
鲁彩江	1. 西南交通大学机械工程学院,2. 先进驱动节能技术教育部工程研究中心成都,3. 轨道交通运维技术与装备四川省重点实验室
曹中清	1. 西南交通大学机械工程学院
刘子轩	1. 西南交通大学机械工程学院
江雪玲	1. 西南交通大学机械工程学院
李林峰	1. 西南交通大学机械工程学院

Author	Institution
Sun Shaoheng	1. School of Mechanical Engineering, Southwest Jiaotong University
Lu Caijiang	1. School of Mechanical Engineering, Southwest Jiaotong University,2. Engineering Research Center of Advanced Drive Energy Saving Technologies, Ministry of Education,3. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province
Cao Zhongqing	1. School of Mechanical Engineering, Southwest Jiaotong University
Liu Zixuan	1. School of Mechanical Engineering, Southwest Jiaotong University
Jiang Xueling	1. School of Mechanical Engineering, Southwest Jiaotong University
Li Linfeng	1. School of Mechanical Engineering, Southwest Jiaotong University

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中文摘要:

在使用电磁感应方法诊断杆塔接地网断点的过程中,针对人工诊断引起的误差问题,提出了一种基于一维卷积神经网络(one dimensional-convolutional neural network, 1D-CNN)的诊断模型,诊断模型以接地网正上方的一维磁场数据为输入,通过深度神经网络输出断点故障的数量和位置。首先通过实验验证了电磁感应方法在杆塔接地网断点诊断问题中的有效性,然后建立了磁场断点故障数据集,之后进行了 1D-CNN 诊断模型的训练。在诊断准确度验证实验中,1D-CNN 诊断模型在 40 个故障磁场样本上达到了 97. 50%的诊断准确率,表现出了良好的泛化性;诊断效果对比实验表明,1D-CNN 诊断模型的 AUC 值达 0. 951,在 3 次随机训练中对各类故障的平均识别率达到了 92. 08%,在 15 次训练中的平均测试集精度达到了 94. 30%,平均每代训练时间 0. 875 0 s,在各项指标上较 DNN、RNN 均有明显优势。

英文摘要:

In the process of using electromagnetic induction method to diagnose the breakpoint of the grounding grid of the tower, aiming at the error caused by manual diagnosis, this paper proposes a diagnosis model based on one dimensional-convolutional neural network (1D-CNN), the diagnosis model takes the one-dimensional magnetic field data directly above the grounding grid as input, and outputs the number and location of breakpoint faults through a deep neural network. This paper firstly verified the effectiveness of electromagnetic induction method in the diagnosis of tower grounding grid breakpoints through experiment, then a magnetic field breakpoint fault dataset was established and a 1D-CNN diagnosis model was trained. In the diagnostic accuracy verification experiment, the diagnostic model reached 97. 50% diagnostic accuracy on 40 faulty magnetic field samples, showing good generalization. The comparison experiment of the diagnosis effect shows that the AUC value of the 1D-CNN diagnosis model reaches 0. 951, the average recognition rate of various faults in three random trainings reaches 92. 08%, and the average test set accuracy in 15 trainings reaches 94. 30%. and the average training time per generation is 0. 875 0 s, which has obvious advantages over DNN and RNN in various indicators.

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