| 基于粗糙集和神经网络的机械故障诊断研究 |
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| 引用本文： | 李爱民,施惠丰.基于粗糙集和神经网络的机械故障诊断研究[J].昆明理工大学学报(理工版),2011,36(1):35-39. |
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| 作者姓名： | 李爱民 施惠丰 |
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| 作者单位： | 1. 海军工程大学,船舶与动力学院,湖北,武汉,430033
2. 91287部队,上海,200083 |
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| 摘 要： | 提出了粗糙集理论与神经网络结合的机械故障诊断方法,研究了连续属性离散化的SOM方法和条件属性约简的差别矩阵方法,归纳了构建神经网络需考虑的关键问题,用一个算例验证了方法的有效性.结果表明：粗糙集能有效地约简冗余信息,简化神经网络的结构,缩短网络的训练时间,提高诊断的效率;SOM网络能将连续性输入映射成具有理想聚类结果的离散性输出,并能保持数据间的拓扑结构不变;利用差别矩阵对决策表进行约简,结果准确可靠;BP神经网络泛函逼近能力强,能快速准确地完成特征空间到故障空间的映射.
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| 关 键 词： | 粗糙集 神经网络 机械故障诊断 |
Study on Mechanical Fault Diagnosis Based on Rough Set Theory and Neural Network |
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| LI Ai-min,SHI Hui-feng.Study on Mechanical Fault Diagnosis Based on Rough Set Theory and Neural Network[J].Journal of Kunming University of Science and Technology(Natural Science Edition),2011,36(1):35-39. |
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| Authors: | LI Ai-min SHI Hui-feng |
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| Affiliation: | LI Ai-min1,SHI Hui-feng2 (1.College of Naval Architecture and Power,Naval University of Engineering,Wuhan 430033,China,2.Unit No.91287,Shanghai 200083,China) |
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| Abstract: | The method of mechanical fault diagnosis based on rough set theory and neural network is put forward in this paper.Firstly,the discretization of continuous attributes by SOM and the reduction of condition attributes by discernibility matrix are studied.Then,the key problems about how to build a neural network are induced.Finally,the method is proved by an example.The results show that rough set theory can effectively get rid of redundant information,simplify the structure,decrease the training time of the n... |
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| Keywords: | rough set theory neural network mechanical fault diagnosis |
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