| 基于熵理论的超声波检测信号消噪与缺陷识别 |
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| 引用本文： | 闫晓玲,董世运,刘彬,徐滨士,王望龙.基于熵理论的超声波检测信号消噪与缺陷识别[J].北京理工大学学报,2012,32(5):465-469. |
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| 作者姓名： | 闫晓玲 董世运 刘彬 徐滨士 王望龙 |
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| 作者单位： | 北京理工大学机械与车辆学院,北京100081;装甲兵工程学院装备再制造技术国防科技重点实验室,北京100072;装甲兵工程学院装备再制造技术国防科技重点实验室,北京,100072 |
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| 基金项目： | 国家自然科学基金资助项目(50975287);国家"九七三"计划项目(2011CB013405) |
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| 摘 要： | 为消除超声检测信号中大量存在的噪声,提高材料内部缺陷诊断的准确性,采用基于熵理论的自适应阈值消噪算法对超声波信号进行消噪处理.分析了基于Shannon熵的最优小波包基搜索算法,提出了用熵表征信号含噪状态,根据小波能谱熵确定小波包不同分解尺度阈值的基本原理.对含缺陷的斯泰尔发动机曲轴的超声信号处理实验结果表明,这种方法对噪声消除比较彻底,能够获得表征缺陷大小、位置的准确信息,提高了材料内部缺陷定量分析的准确度.
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| 关 键 词： | 熵 自适应阈值 消噪 缺陷识别 |
| 收稿时间： | 2011/10/26 0:00:00 |
De-Noising of Ultrasonic Signals Based on Entropy Theory and Recognition of Defect in Material |
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| YAN Xiao-ling,DONG Shi-yun,LIU Bin,XU Bin-shi and WANG Wang-long.De-Noising of Ultrasonic Signals Based on Entropy Theory and Recognition of Defect in Material[J].Journal of Beijing Institute of Technology(Natural Science Edition),2012,32(5):465-469. |
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| Authors: | YAN Xiao-ling DONG Shi-yun LIU Bin XU Bin-shi and WANG Wang-long |
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| Institution: | School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;Science and Technology on Remanufacturing Laboratory, Academy of Armored Forces Engineering, Beijing 100072, China;Science and Technology on Remanufacturing Laboratory, Academy of Armored Forces Engineering, Beijing 100072, China;Science and Technology on Remanufacturing Laboratory, Academy of Armored Forces Engineering, Beijing 100072, China;Science and Technology on Remanufacturing Laboratory, Academy of Armored Forces Engineering, Beijing 100072, China;Science and Technology on Remanufacturing Laboratory, Academy of Armored Forces Engineering, Beijing 100072, China |
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| Abstract: | In order to eliminate the noise which exists in ultrasonic detection signal and improve the diagnostic accuracy of defects inside the material, the method of de-noising ultrasonic signal by applying adaptive threshold(EAT) based on entropy theory is put forward in this paper. The searching algorithm of best wavelet packet basis adopting Shannon entropy is analyzed. The state of signal with noise is characterized by entropy and the threshold of wavelet packet decomposed in different scales is determined according to the entropy of the wavelet packet energy spectrum. Experiment of processing ultrasonic signal which comes from Steyr engine crankshaft with flaws has been implemented. Information that characterizes defect size and location could be extracted accurately from the processing results. The result indicates that the proposed EAT method has better de-noising performance and it has benefit to enhancing the degree of accuracy for quantitatively analyzing the defect inside material. |
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| Keywords: | entropy adaptive threshold de-noising defect recognition |
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