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浙江大学学报(工学版)
J4  2011, Vol. 45 Issue (5): 846-850    DOI: 10.3785/j.issn.1008-973X.2011.05.012
机械工程     
基于负熵最大化的机械振源半盲分离方法
周晓峰,杨世锡
浙江大学 机械工程学系,浙江 杭州 310027
Semi-blind sources separation of mechanical vibrations base on
maximization of negentropy
ZHOU Xiao-feng, YANG Shi-xi
Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
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摘要：

为了快速、有效地分离传感器观测信号中的机械振源信号,提出一种负熵最大化的机械振源半盲分离方法.该方法根据目标振源的振动特性构造相应的参考源信号,将参考源信号和分离的目标振源信号的均方误差作为约束条件引入到盲源分离的对照函数中,通过求解约束最优问题,实现目标机械振源信号的分离.试验结果表明,基于负熵最大化的半盲分离方法能快速、有效地分离出目标振源信号,为机械振动信号的监测与故障诊断提供一种新的方法和思路.
Abstract:

In order to separate mechanical vibration sources form sensor signals rapidly and effectively, a novel method was proposed, which was based on maximization of negentropy for semi-blind sources separation of mechanical vibrations. The reference signals that carry some information of sources were constructed. The mean square error between reference signals and separated sources was incorporated into contrast function as the constraints. The interested mechanical vibration source was obtained by solving the constrained optimization problem. The proposed method was compared with the conventional BSS method, and the experiment results showed that the proposed method is very effective. It is possible to apply the new method to vibration signals analysis and mechanical fault diagnosis．
出版日期: 2011-11-24
:  TN 911.7  
基金资助:

国家自然科学基金资助项目（50675194）；国家“863”高技术研究发展计划资助项目（2008AA04Z410）；国家科技重大专项资助项目（2009ZX04014-101-01）.
通讯作者: 杨世锡，男，教授.     E-mail: Yangsx@zju.edu.cn
作者简介: 周晓峰（1974-），男，浙江宁波人，博士生，从事智能检测与信号处理、机械状态监测与故障诊断研究.E-mail:zxf@zju.edu.cn
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引用本文:

周晓峰,杨世锡. 基于负熵最大化的机械振源半盲分离方法[J]. J4, 2011, 45(5): 846-850.

ZHOU Xiao-feng, YANG Shi-xi. Semi-blind sources separation of mechanical vibrations base on
maximization of negentropy. J4, 2011, 45(5): 846-850.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2011.05.012    或    https://www.zjujournals.com/eng/CN/Y2011/V45/I5/846

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