| 基于改进D-S的汽轮机组集成故障诊断研究 |
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| 引用本文： | 徐春梅,张浩,彭道刚.基于改进D-S的汽轮机组集成故障诊断研究[J].系统仿真学报,2011,23(10):2190-2194,2199. |
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| 作者姓名： | 徐春梅 张浩 彭道刚 |
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| 作者单位： | 1. 同济大学CIMS研究中心,上海200092／上海电力学院电力与自动化工程学院,上海200090
2. 上海电力学院电力与自动化工程学院,上海,200090 |
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| 基金项目： | 国家自然科学基金重点项目(61034004); 上海市青年科技启明星计划项目资助(10QA1402900); 上海市创新行动计划部分地方院校能力建设专项项目(10250502000) |
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| 摘 要： | 在分析与比较多个D-S合成规则的基础上，结合汽轮机组故障的特点，提出了一种基于改进D-S证据理论的集成故障诊断方法。该方法利用改进的D-S理论来表示和处理不确定的、模糊的信息，利用灰色理论和GRNN（广义回归神经网络）网络来处理证据理论中的基本概率分配问题，充分发挥灰色理论和GRNN的优点，提高故障诊断率。仿真结果表明，所提曲的集成故障诊断方法能有效地诊断汽轮机纽的故障，决策合理，可信度高，且能避免误诊现象，具有庭好的应用前景。
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| 关 键 词： | 灰色关联度 GRNN 改进D-S证据理论 信息融合 故障诊断 汽轮机 |
Fault Diagnosis Method Based on Improved D-S Evidential Theory for Turbine Generator Unit |
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| XU Chun-mei,ZHANG Hao,PENG Dao-gang.Fault Diagnosis Method Based on Improved D-S Evidential Theory for Turbine Generator Unit[J].Journal of System Simulation,2011,23(10):2190-2194,2199. |
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| Authors: | XU Chun-mei ZHANG Hao PENG Dao-gang |
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| Institution: | XU Chun-mei1,2,ZHANG Hao1,PENG Dao-gang2(1.CIMS Research Center,Tongji University,Shanghai 200092,China,2.School of Power and Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China) |
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| Abstract: | Based on analyzing and comparing various D-S compound rules and combining the feature of turbine generator sets,an integrated fault diagnosis method was proposed.This method dealt with the uncertain and fuzzy information by improved D-S evidence theory,and solved the basic probability assignment of evidence theory by grey theory and generalized regression neural network(GRNN),which made advantage of grey theory and GRNN and improves the rate of fault diagnosis.The simulation results show that the proposed m... |
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| Keywords: | gray relational grade GRNN improved D-S evidential theory information fusion fault diagnosis turbine |
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