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A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

机译：基于CBR的电力设备智能故障诊断模型。

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摘要

Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment's running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.
机译：如今，随着电力行业的快速发展，对电源可靠性的需求已大大增加。然而，如此巨大的需求需要大量的电网来维持。因此，包含大量信息的电力设备的运行和测试数据为在线监控和故障诊断奠定了基础，从而最终实现状态维护。本文提出了一种基于案例推理的电力设备智能故障诊断模型。该模型旨在通过数据挖掘来发现设备故障的潜在规则。智能模型通过分析以下四类数据来构建设备的条件案例库：在线记录数据，历史数据，基本测试数据和环境数据。 SVM回归分析还用于挖掘案例库，以进一步建立设备状态指纹。通过这种状态指纹可以诊断设备的运行数据，以检测是否存在故障。最后，本文基于一组实际数据验证了智能模型和三比率方法。研究结果表明，该智能模型在故障诊断中更有效，更准确。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|203083.1-203083.9|共9页
  • 作者

    Ma Gang; Jiang Linru; Xu Guchao; Zheng Jianyong;

  • 作者单位

    Southeast Univ, Elect Engn, Nanjing 210096, Jiangsu, Peoples R China.;

    Nanjing Normal Univ, Sch Elect & Automat Engn, Nanjing 210042, Jiangsu, Peoples R China.;

    Nanjing Normal Univ, Sch Elect & Automat Engn, Nanjing 210042, Jiangsu, Peoples R China.;

    Southeast Univ, Elect Engn, Nanjing 210096, Jiangsu, Peoples R China.;

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