| 基于改进BP算法的汽轮发电机组的故障诊断 |
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| 引用本文： | 陈仓,傅行军. 基于改进BP算法的汽轮发电机组的故障诊断[J]. 汽轮机技术, 2006, 48(3): 227-229 |
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| 作者姓名： | 陈仓 傅行军 |
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| 作者单位： | 东南大学火电机组振动国家工程研究中心,南京,210096;东南大学火电机组振动国家工程研究中心,南京,210096 |
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| 摘 要： | 针对传统BP算法即梯度下降法,收敛速度慢、容易陷入局部极小值等缺点,提出了基于附加动量法和自适应学习速率的改进方法。将改进后的BP神经网络应用于汽轮发电机组的故障诊断中,获得了很好的效果,证明了改进方法的有效性。
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| 关 键 词： | 神经网络 故障诊断 汽轮发电机组 附加动量法 自适应学习速率 |
| 文章编号： | 1001-5884（2006）03-0227-03 |
| 收稿时间： | 2005-10-08 |
| 修稿时间： | 2005-10-08 |
Improved BP Algorithm for Fault Diagnosis of Steam-Turbine Generator Unit |
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| CHEN Cang,FU Xing-jun. Improved BP Algorithm for Fault Diagnosis of Steam-Turbine Generator Unit[J]. Turbine Technology, 2006, 48(3): 227-229 |
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| Authors: | CHEN Cang FU Xing-jun |
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| Affiliation: | National Engineering Research Center of Turbo-generator Vibration, Southeast University, Nanjing, 210096, China |
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| Abstract: | Aiming at the defect of gradient-descending arithmetic,such as low-speed of constringency,easily falling into local minimum,an improved approach based on affixation momentum and self-adaptive learning rate is proposed.The results show the validity of the approach,which is applied in fault diagnosis of turbine-generator unit. |
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| Keywords: | neural network fault diagnosis turbine-generator unit affixation momentum self-adaptive learning rate |
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