| 基于粗糙集与人工神经网络的烟气机故障诊断 |
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| 引用本文： | 舒服华.基于粗糙集与人工神经网络的烟气机故障诊断[J].化工设备与管道,2006,43(1):43-46. |
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| 作者姓名： | 舒服华 |
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| 作者单位： | 武汉理工大学,机电工程学院,武汉,430070 |
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| 摘 要： | 提出了一种粗糙集理论与神经网络集成的烟气机故障诊断方法。首先应用SOM网络对故障诊断数据中的连续属性进行离散化,然后根据粗糙集理论,借助遗传算法进行故障诊断决策系统约简,获得最优决策系统。最后在最优决策系统的基础上,设计RBF神经网络对烟气机故障进行诊断。试验结果显示,该方法可以有效提高烟气机故障诊断的精度和效率。
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| 关 键 词： | 粗糙集 神经网络 烟气机 故障诊断 |
| 文章编号： | 1009-3281(2006)01-0043-04 |
| 收稿时间： | 2005-10-21 |
| 修稿时间： | 2005年10月21 |
Fault Diagnosis of Turbine Machine Based on Rough Set and Artificial Neural Net Work |
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| Shu Fuhua.Fault Diagnosis of Turbine Machine Based on Rough Set and Artificial Neural Net Work[J].Process Equipment & Piping,2006,43(1):43-46. |
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| Authors: | Shu Fuhua |
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| Abstract: | A new hybrid system of rough set and neural network for intelligent fault diagnosis was presented in this paper.Firstly,the continuous properties in diagnostic decision system were made discrete by use of self-organizing map neural network.Then,based on rough set theory and with GA,the diagnostic system was simplified to obtain optimum decision system.Finally,with this decision system,RBF neural net work was designed to diagnose the faults occurred in turbine machine.The test results has showed that this method can efficiently enhance the precision of fault diagnosis for turbine machine. |
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| Keywords: | rough set neural net work turbine machine fault diagnosis |
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