基于改进形态分量分析的齿轮箱轴承多故障诊断研究

李 辉;郑海起;唐力伟

振动与冲击 ›› 2012, Vol. 31 ›› Issue (12) : 135-140.

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PDF(1658 KB)
振动与冲击 ›› 2012, Vol. 31 ›› Issue (12) : 135-140.
论文

基于改进形态分量分析的齿轮箱轴承多故障诊断研究

  • 李 辉1; 郑海起2; 唐力伟2

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Bearing Multi-faults Diagnosis Based on Improved Morphological Component Analysis

  • Li Hui1; Zheng Hai-qi2; Tang Li-wei2

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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。

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 in the data based on their morphology. According to the shortcomings of traditional morphological component analysis which the dictionary need to be selected manually and threshold selection, an improved approach to MCA combining adaptive dictionary and hard threshold MOM strategy is proposed. The simulative and experimental results show that not only the component of morphological diversity is separated, but also the signal noise ratio of separated signal is improved, the multi-faults of the bearing of a gearbox can be effectively detected.

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

Key words

Morphological component analysis / Sparse representation / Fault diagnosis / Bearing / Independent component analysis / Signal processing

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导出引用
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
Li Hui;Zheng Hai-qi;Tang Li-wei. Bearing Multi-faults Diagnosis Based on Improved Morphological Component Analysis [J]. Journal of Vibration and Shock, 2012, 31(12): 135-140

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