| 机械故障信号的分离 |
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| 引用本文： | 陈岳东,蒋林.机械故障信号的分离[J].中国机械工程,1995,6(2):48-49. |
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| 作者姓名： | 陈岳东 蒋林 |
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| 作者单位： | 西安交通大学诊断与控制学研究所 |
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| 基金项目： | 国家“八五”重点科技攻关项目 |
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| 摘 要： | 针对机械故障信号经常是多种故障信号的混合，给正确的故障识别造成很大困难的实际情况，提出基于神经网络非线性主分量分析的机构故障信号分离方法。阐述了故障信息的分离与主分量分析的关系。并将二者统一起来，从理论上证明应用主分量分析方法进行故障分离的有效性；介绍神经网络非线性分离，取得令人满意的结果。
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| 关 键 词： | 机械 故障信号分离 主分量 神经网络 故障诊断 |
Separation of the Machine Fault Information |
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| Chen Yuedong, Jiang Lin, Qu Liangsheng.Separation of the Machine Fault Information[J].China Mechanical Engineering,1995,6(2):48-49. |
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| Authors: | Chen Yuedong Jiang Lin Qu Liangsheng |
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| Affiliation: | Chen Yuedong; Jiang Lin; Qu Liangsheng |
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| Abstract: | Nonlinear principal component analysis via neural network is taken to separate the fault information of large rotating machinery. The relationship between the separation of faults and principal component analysis(PCA) is described. The nonlinear principal component analysis by means of neural network is described briefly. The practical examples are given. The results show that the nonlinear principal component analysis succeefully reduces dimension and complexity,a satisfaCtory separated result is acquired. |
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| Keywords: | s: fault information separation principal component analysis neural network nonlinear fault diagnosis |
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