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应用科学学报 ›› 2010, Vol. 28 ›› Issue (1): 32-37.

• 信号与信息处理 • 上一篇    下一篇

改进的EMD算法及其在条带间距测量中的应用

李凌1， 黎明1;2， 鲁宇明2   

  1. 1. 南京航空航天大学自动化学院，南京210016
    2. 南昌航空大学无损检测教育部重点实验室，南昌330063
  • 收稿日期:2009-09-08 修回日期:2009-10-09 出版日期:2010-01-20 发布日期:2010-01-20
  • 作者简介:李凌，博士生，研究方向：断口图像处理、模式识别等，E-mail: tina@nuaa.edu.cn；黎明，教授，博导，研究方向：进化计算、图像处理与模式识别，E-mail: limingniat@hotmail.com
  • 基金资助:

    国家自然科学基金(No.60963002)；航空科学基金(No.2008ZD56003)； 江西省教育厅科技项目基金(No.GJJ09483, No.GJJ
    08209)资助

Improved EMD Algorithm and Its Application to Striation Distance Measurement

LI Ling1, LI Ming1;2, LU Yu-ming2   

  1. 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
    Nanjing 210016, China
    2. Key Laboratory of Nondestructive Testing of Ministry of Education, Nanchang Hangkong University,
    Nanchang 330063, China
  • Received:2009-09-08 Revised:2009-10-09 Online:2010-01-20 Published:2010-01-20

PDF

2089

被引次数

13

摘要：

经验模式分解(EMD)是一种非平稳信号分析方法，存在边缘效应. 针对此问题，文中用线性预测对信号进行端点延拓，增加附加的极值点来拟合包络线，以实现准确的经验模式分解，并通过数值实验验证了该方法的有效性. 根据疲劳断口图像中条带间的距离呈准周期性，可对其进行经验模式分解，分别算出水平和垂直方向上的条带间距，并通过三角转换关系得到相邻条带间的实际距离. 对实际疲劳断口图像中条带间距的测量表明，把改进的经验模式分解应用到条带间距测量是有效的.

 

关键词: 经验模式分解, 线性预测, 疲劳断口图像, 条带间距

Abstract:

Empirical mode decomposition (EMD) is a useful method in nonlinear and non-stationary signal analysis. We propose an improvement to the EMD method to reduce the end effects based on linear prediction. Data are extended by linear prediction until a local extreme appears at each end. The same procedure is applied to each intrinsic mode function. Experiments show that the proposed method works well in restraining the end effects of EMD compared to other methods. Because striation distance in fatigue fracture image is quasi-periodical, striation distance in the horizontal and vertical directions are calculated based on improved EMD algorithm, and the real striation distances obtained through triangle conversion. The striation distance measurement of actual images shows that application of EMD to striation distances measurement is feasible.

Key words: empirical mode decomposition (EMD), linear prediction, fatigue fracture image, striation distance

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