基于双谱熵模型的故障模式识别

doi:10.3969/j.issn.1000-1093.2012.06.014

兵工学报 ›› 2012, Vol. 33 ›› Issue (6): 718-723.doi: 10.3969/j.issn.1000-1093.2012.06.014

基于双谱熵模型的故障模式识别

黄晋英^1,2，潘宏侠¹，毕世华²，崔宝珍¹

(1.中北大学机械工程与自动化学院, 山西太原 030051; 2.北京理工大学宇航学院, 北京 100081)

收稿日期:2010-03-05 修回日期:2010-03-05 上线日期:2014-03-04
作者简介:黄晋英（1971—），女，教授，博士生导师
基金资助:
国家自然科学基金项目（50875247）；山西省自然科学基金项目（2009011026-1）

Fault Pattern Recognition Based on Bispectrum Entropy Model

HUANG Jin-ying^1,2， PAN Hong-xia¹， BI Shi-hua²， CUI Bao-zhen¹

(1.School of Mechanical Engineering and Automation, North University of China, Taiyuan 030051, Shanxi, China;2.School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China)

Received:2010-03-05 Revised:2010-03-05 Online:2014-03-04

摘要/Abstract

摘要： 提出利用双谱计算信号的双谱熵，并作为特征参量进行故障模式识别的方法。分析了振动信号双谱的特征，在子空间分布概率下，推导了基于能量分布的双谱熵计算方法。在理论推导分析的基础上，进行了某齿轮箱在4种工况下的振动信号提取实验，建立了齿轮箱故障模式识别BP神经网络。最后将双谱熵特征参量作为输入，对设置了4种故障工况的齿轮箱进行了故障模式识别，成功地判别了4种工况，验证了方法的有效性。

关键词: 信息处理技术, 双谱熵, 故障, 模式识别, 特征参量, 齿轮箱

Abstract: A fault pattern recognition method was developed on the basis of information entropy and bispectrum theory. The bispectrum features of vibration signal were analyzed. And a bispectrum entropy algorithm based on energy distribution was derived under the condition of subspace distribution probability. Then, the vibration signals of a gearbox under four conditions were extracted experimentally. And a BP neural network for the fault pattern recognition was established by using the bispectrum entropy feature as input. Finally, this method was verified by successfully recognizing four fault patterns of the gearbox.

中图分类号:

TH115

黄晋英，潘宏侠，毕世华，崔宝珍. 基于双谱熵模型的故障模式识别[J]. 兵工学报, 2012, 33(6): 718-723.

HUANG Jin-ying， PAN Hong-xia， BI Shi-hua， CUI Bao-zhen. Fault Pattern Recognition Based on Bispectrum Entropy Model[J]. Acta Armamentarii, 2012, 33(6): 718-723.

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基于双谱熵模型的故障模式识别

Fault Pattern Recognition Based on Bispectrum Entropy Model

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