| 基于小波分析与人工神经网络的水轮机压力脉动信号分析 |
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| 引用本文： | 赵林明,楚清河,代秋平,王利英.基于小波分析与人工神经网络的水轮机压力脉动信号分析[J].水利学报,2011,42(9):1075-1080. |
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| 作者姓名： | 赵林明 楚清河 代秋平 王利英 |
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| 作者单位： | 1. 河北工程大学,河北邯郸,056021
2. 华北水利水电学院,河南郑州,450011 |
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| 基金项目： | 国家自然科学基金项目(60940036); 河北省自然科学基金项目(E2010001026) |
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| 摘 要： | 针对水轮机尾水管压力脉动信号的非平稳和时变特性,提出了一种基于小波分析和自组织人工神经网络相结合的尾水管压力脉动信号的分析方法。这种方法首先应用小波阈值法对信号进行降噪减少干扰,然后将小波分解系数重构得到不同频带的信号分量,并提取显著的不同频带能量,最后将各频带能量作为特征向量,用自组织人工神经网络进行模式识别,得到了...
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| 关 键 词： | 水轮机 小波分析 自组织人工神经网络 模式识别 |
Analysis of pressure fluctuation in draft tube based on wavelet analysis and artificial neural networks |
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| ZHAO Lin-ming,CHU Qing-he,DAI Qiu-ping and WANG Li-ying.Analysis of pressure fluctuation in draft tube based on wavelet analysis and artificial neural networks[J].Journal of Hydraulic Engineering,2011,42(9):1075-1080. |
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| Authors: | ZHAO Lin-ming CHU Qing-he DAI Qiu-ping and WANG Li-ying |
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| Affiliation: | ZHAO Lin-ming1,CHU Qing-he2,DAI Qiu-ping1,WANG Li-ying1(1.Hebei University of Engineering,Handan 056021,China,2.North China Institute of Water Conservancy and Hydroelectric Power,Zhengzhou 450011,China) |
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| Abstract: | In view of the non-stationary and time-varying characteristics of the pressure fluctuation signal in draft tube,this paper presents a method combining wavelet analysis with a self-organizing artificial neural network to analysis the pressure fluctuation signal.Firstly,the wavelet threshold value method was used to decrease the noise and reduce interference,then the wavelet coefficients were reconstructed to obtain signal component of different frequency band and extract significant different band energy.The... |
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| Keywords: | hydraulic turbine wavelet analysis self-organizing artificial neural networks pattern recognition |
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