频带能量特征法在声发射刀具磨损监测系统中的应用
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摘要
基于对声发射(AE)信号特点的分析和小波包分解理论对不平稳信号特征提取的优势,提出一种利用AE信号的能量变化来监测刀具磨损状态的方法。该方法利用db8小波基对AE信号进行5层小波包分解,将分解后各频带上的能量值作为特征参数,并组成特征向量。分别提取在新刀和刀具磨损状态下的特征向量,根据其变化即可判别刀具磨损的程度。试验结果验证了该方法在刀具磨损判析中的可用性。
Based on the characteristic analysis of acoustic emission(AE)signals and the advantages of wavelet packets decomposition theory in the nonsteady signal feature extraction,a method to judge the cutter wear state by means of the energy variable of AE signals is proposed.The method uses db8 wavelet packet to decompose AE signal into 5 level,taking the decomposed signal energy in frequency bands as characteristic parameters,and then use these characteristic parameters to compose characteristic vector.The characteristic vectors under new cutter condition and cutter wear condition are extracted respectively,so that the cutter wear degree can be distinguished according to the variance contribution of character values.The usability of this method is verified by the test results.
Based on the characteristic analysis of acoustic emission(AE)signals and the advantages of wavelet packets decomposition theory in the nonsteady signal feature extraction,a method to judge the cutter wear state by means of the energy variable of AE signals is proposed.The method uses db8 wavelet packet to decompose AE signal into 5 level,taking the decomposed signal energy in frequency bands as characteristic parameters,and then use these characteristic parameters to compose characteristic vector.The characteristic vectors under new cutter condition and cutter wear condition are extracted respectively,so that the cutter wear degree can be distinguished according to the variance contribution of character values.The usability of this method is verified by the test results.
引文
1赵冬梅,朱目成,江涌等.小波理论及其在AE信号处理中应用现状.传感器世界,2006(9):11～15
2Qing Qing Ni,Yasuharu Misada.Analysis of AE signals by wavelet transform.Journal of Society Material Science,1998,47(3):305～311
3陈晓智,李蓓智,杨建国.一种新的AE刀具磨损小波分析方法.无损检测,2007,29(1):12～14
4李宏男,孙鸿敏.基于小波分析和神经网络的框架结构损伤诊断方法.地震工程与工程振动,23(5):141～148
5金鑫,施展.基于小波的AE信号参数提取方法.仪器仪表学报,2006,27(6):347～348
6谢秀娴,付攀,曹伟青.AE和小波包分解技术在刀具磨损状态中的应用.中国测试技术,2006,32(2):40～42
2Qing Qing Ni,Yasuharu Misada.Analysis of AE signals by wavelet transform.Journal of Society Material Science,1998,47(3):305～311
3陈晓智,李蓓智,杨建国.一种新的AE刀具磨损小波分析方法.无损检测,2007,29(1):12～14
4李宏男,孙鸿敏.基于小波分析和神经网络的框架结构损伤诊断方法.地震工程与工程振动,23(5):141～148
5金鑫,施展.基于小波的AE信号参数提取方法.仪器仪表学报,2006,27(6):347～348
6谢秀娴,付攀,曹伟青.AE和小波包分解技术在刀具磨损状态中的应用.中国测试技术,2006,32(2):40～42
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