| 改进人工鱼群优化粗糙集的水电机组故障诊断 |
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| 引用本文： | 付波,黄英伟,程琼,邢鑫.改进人工鱼群优化粗糙集的水电机组故障诊断[J].湖北工业大学学报,2012,27(1):92-95. |
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| 作者姓名： | 付波 黄英伟 程琼 邢鑫 |
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| 作者单位： | 1. 湖北工业大学电气与电子工程学院,湖北武汉430068／东莞华中科技大学制造工程研究院,广东东莞523000
2. 湖北工业大学电气与电子工程学院,湖北武汉,430068 |
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| 基金项目： | 湖北省自然科学基金项目，广东省工业攻关项目 |
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| 摘 要： | 针对水电机组大量的现场监测数据信息，基于传统的人工智能方法对故障信息不能及时有效地分析的问题，提出了一种基于改进人工鱼群优化粗糙集的水电机组故障诊断方法．首先，利用鱼群的寻优聚群行为对连续属性进行离散化，然后采用粗糙集理论对离散化后的决策表进行约简，建立故障诊断规则决策表，再用提取的规则对水电机组故障进行诊断．仿真结果表明：与传统方法相比，该算法能够提高水电机组故障诊断的准确率．
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| 关 键 词： | 水电机组 故障诊断 改进人工鱼群 粗糙集 规则 |
Fault Diagnosis of Hydro-turbine Generating Unit using Modified AFSA-based Rough Set Theory |
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| FU Bo,HUANG Ying-wei,CHENG Qiong,XING Xin.Fault Diagnosis of Hydro-turbine Generating Unit using Modified AFSA-based Rough Set Theory[J].Journal of Hubei University of Technology,2012,27(1):92-95. |
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| Authors: | FU Bo HUANG Ying-wei CHENG Qiong XING Xin |
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| Affiliation: | 1(1 School of Electrical and Electric Engineering,Hubei Univ.of Technology,Wuhan,430068,China;2 DG-HUST Manufacturing Ingineering Institute,Dongguan 523000,China) |
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| Abstract: | Due to the fact that the traditional artificial intelligence methods cannot effectively and timely analysis or can not be accurately diagnosed or misdiagnosed because of the ill-conditioned problem caused by inefficient discretization approaches,based on a large number of on-site monitoring data,a method based on rough set theory integrated with improved artificial fish-swarm algorithm(AFSA) was presented in this paper for fault diagnosis of hydro-turbine generating unit.Firstly,the improved artificial fish-swarm algorithm was used to discrete continuous attribute,and then the rough set theory was used to reduce the decision table.Therefore,the rules could be ued to diagnose the faults.The simulation results indicated that the method increased the diagnosis accuracy. |
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| Keywords: | hydro-turbine generating unit improved artificial fish-swarm rough set fault diagnosis rule |
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