Machine learning based models for fault detection in automatic meter reading systems

机译：基于机器学习的自动抄表系统中的故障检测模型

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Recently, research has focused on the area of fault detection in Automatic Meter Reading (AMR) systems. The manufacturers and users of AMR systems are now keen to include diagnostic features in the systems to improve salability and reliability. However, traditional manual fault detection methods are time-consuming and inaccurate. automatic fast fault detection methods are urgently needed. In this paper, we propose several machine learning based fault detection models to meet this requirement. Furthermore, we use novel boosting strategy to fuse multiple models to leverage multi-aspect information in AMR systems. The experimental results on simulated data show that the proposed models are accurate and robust, and fusion strategy indeed improve the performance on fault detection.

机译：最近，研究集中在自动抄表（AMR）系统中的故障检测领域。 AMR系统的制造商和用户现在渴望在系统中包含诊断功能，以提高可销售性和可靠性。但是，传统的手动故障检测方法既费时又不准确。迫切需要自动快速故障检测方法。在本文中，我们提出了几种基于机器学习的故障检测模型来满足这一要求。此外，我们使用新颖的提升策略来融合多个模型，以利用AMR系统中的多方面信息。仿真数据的实验结果表明，所提出的模型准确，鲁棒，融合策略确实提高了故障检测的性能。

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来源
《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|684-689|共6页
会议地点 Shenzhen(CN)
作者
Yinggang Kou; Gaoying Cui; Jie Fan; Xiao Chen; Wei Li;
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作者单位

State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103;

State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103;

State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103;

State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103;

State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Decision trees; Fault detection; Recurrent neural networks; Servers; Data models; Security; Pattern analysis;

机译：决策树;故障检测;递归神经网络;服务器;数据模型;安全性;模式分析;;

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Machine learning based models for fault detection in automatic meter reading systems

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