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基于改进形态分量分析的齿轮箱轴承多故障诊断研究
引用本文:李辉,郑海起,唐力伟.基于改进形态分量分析的齿轮箱轴承多故障诊断研究[J].振动与冲击,2012,31(12):135-140.
作者姓名:李辉  郑海起  唐力伟
作者单位:1石家庄铁路职业技术学院机电工程系,石家庄 050041;2军械工程学院一系,石家庄 050003
基金项目:国家自然科学基金资助项目(50975185,50775219)
摘    要:形态分量分析是一种基于信号形态多样性和信号稀疏表示的信号和图像处理方法,其主要目标是根据信号组成成分的形态差异性,选择合适的字典来分离信号。针对传统形态分量分析的字典选择和阈值选择的缺陷,提出了基于自适应字典选择和TH-MOM (Hard Threshold-MOM)的阈值更新策略,通过仿真信号和齿轮箱轴承多故障振动实验信号的研究结果表明:该方法不仅能将形态各异的多分量信号进行有效分离,提高了信噪比,而且提高了从强噪声环境中提取瞬态冲击特征的能力,能有效地识别轴承的故障类型和部位。

关 键 词:形态分量分析    稀疏表示    故障诊断    轴承    独立分量分析    信号处理  
收稿时间:2011-4-18
修稿时间:2011-7-12

Bearing multi-fault diagnosis based on improved morphological component analysis
LI Hui,ZHENG Hai-qi,TANG Li-wei.Bearing multi-fault diagnosis based on improved morphological component analysis[J].Journal of Vibration and Shock,2012,31(12):135-140.
Authors:LI Hui  ZHENG Hai-qi  TANG Li-wei
Affiliation:1 Department of Electromechanical Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang 050041;2 First Department, Ordnance Engineering College, Shijiazhuang 050003
Abstract:Morphological component analysis(MCA) is a signal-or image-processing method based on signal morphological diversity and sparse representation.MCA takes advantage of the sparse representation of the analyzed data in over-complete dictionaries to separate features of the data based on their morphology.Aiming at shortcomings of traditional morphological component analysis about dictionary selection and threshold selection,an improved approach to MCA combining adaptive dictionary selection and hard threshold MOM strategy was proposed.The simulation and experimental results showed that with the proposed method,not only a signal with morphological diversity can be separated,but also the signal noise ratio of the separated signal can be improved,the multi-fault of the bearing of a gearbox can be effectively detected.
Keywords:morphological component analysis  sparse representation  fault diagnosis  bearing  independent component analysis  signal processing
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