| 基于改进的邻域粗糙集与概率神经网络的水电机组振动故障诊断 |
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| 引用本文： | 谢玲玲,雷景生,徐菲菲.基于改进的邻域粗糙集与概率神经网络的水电机组振动故障诊断[J].上海电力学院学报,2016,32(2):181-187. |
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| 作者姓名： | 谢玲玲 雷景生 徐菲菲 |
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| 作者单位： | 上海电力学院 电子与信息工程学院,上海电力学院 计算机科学与技术学院,上海电力学院 计算机科学与技术学院 |
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| 摘 要： | 提出了一种基于改进的邻域粗糙集与概率神经网络的水电机组振动故障诊断方法.该方法将邻域粗糙集中的近似精度与信息论观点中的条件熵结合,提出近似条件熵的属性约简算法,减少故障冗余信息,得到最优决策表,并将得到的最优决策表作为概率神经网络(PNN)的训练样本,提高了PNN的训练速度和诊断效率,通过实验证明了所述方法的可行性和有效性.
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| 关 键 词： | 邻域粗糙集 近似条件熵 属性约简 概率神经网络 故障诊断 |
| 收稿时间： | 2015/8/27 0:00:00 |
Vibrant Fault Diagnosis for Hydro-turbine Generating Unit Based on Improved Neighborhood Rough Sets and PNN |
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| XIE Lingling,LEI Jingsheng and XU Feifei.Vibrant Fault Diagnosis for Hydro-turbine Generating Unit Based on Improved Neighborhood Rough Sets and PNN[J].Journal of Shanghai University of Electric Power,2016,32(2):181-187. |
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| Authors: | XIE Lingling LEI Jingsheng and XU Feifei |
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| Affiliation: | School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China,School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China and School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China |
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| Abstract: | A diagnosis method of improved neighborhood rough sets and PNN is proposed to achieve vibrant fault diagnosis for hydro-turbine generating unit.This method obtains the approximate condition entropy by uniting approximation accuracy of neighborhood rough set and condition entropy of information theory,which reduces the redundant information,acquires the optimal decision table.Then the table is the best decision as probabilistic neural network (PNN) training samples to improve the speed and efficiency of diagnosis.Finally,the experimental analysis and comparison show the feasibility and effectiveness of the method. |
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| Keywords: | neighborhood rough sets approximation condition entropy attribute reduction probabilistic neural network fault diagnosis |
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