基于LCD的自适应小波脊线解调及齿轮故障诊断
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摘要
针对最佳小波参数的设定和齿轮裂纹故障振动信号频率成分复杂、信噪比低等问题,将遗传优化算法、小波脊线解调与局部特征尺度分解(local characteristic-scale decomposition,简称LCD)相结合,提出了基于LCD的自适应小波脊线解调方法。首先,采用LCD方法将原始信号分解为若干个内禀尺度分量(intrinsic scale component,简称ISC),并通过选择蕴含特征信息的ISC来实现信号降噪;然后,以小波能量熵为目标函数,采用遗传算法优化小波参数,得到自适应小波;最后,通过自适应小波分析提取ISC的小波脊线,从而实现对原始信号的解调分析。通过齿轮裂纹故障诊断实例验证了该方法的有效性和优越性。
The wavelet ridge can effectively extract the modulation features of the transient signal.However,the demodulation effect is greatly influenced by wavelet parameters.Local characteristic-scale decomposition(LCD)is a new time-frequency analysis method.By combining LCD de-noising and a genetic algorithm with wavelet ridge demodulation,agear incipient fault diagnosis method using the LCD de-noising approach and adaptive wavelet ridge demodulation is proposed to extract transient information from the original signal with a low signal to noise ratio.Furthermore,the proposed method is applied to gear crack fault diagnosis.Firstly,a multi-component AM-FM is adaptively decomposed into a series of intrinsic scale components(ISC).At the same time,the special intrinsic scale component that contains abundant feature information is selected to realize denoising.Secondly,the genetic algorithm is used to optimize wavelet parameters by wavelet energy entropy,thus attaining an adaptive wavelet.Lastly,the adaptive wavelet ridge demodulation method is used to extract instantaneous amplitude and instantaneous frequency.Experimental data analysis results show that the proposed method can be applied to gear crack fault diagnosis.
The wavelet ridge can effectively extract the modulation features of the transient signal.However,the demodulation effect is greatly influenced by wavelet parameters.Local characteristic-scale decomposition(LCD)is a new time-frequency analysis method.By combining LCD de-noising and a genetic algorithm with wavelet ridge demodulation,agear incipient fault diagnosis method using the LCD de-noising approach and adaptive wavelet ridge demodulation is proposed to extract transient information from the original signal with a low signal to noise ratio.Furthermore,the proposed method is applied to gear crack fault diagnosis.Firstly,a multi-component AM-FM is adaptively decomposed into a series of intrinsic scale components(ISC).At the same time,the special intrinsic scale component that contains abundant feature information is selected to realize denoising.Secondly,the genetic algorithm is used to optimize wavelet parameters by wavelet energy entropy,thus attaining an adaptive wavelet.Lastly,the adaptive wavelet ridge demodulation method is used to extract instantaneous amplitude and instantaneous frequency.Experimental data analysis results show that the proposed method can be applied to gear crack fault diagnosis.
引文
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[3]Cheng Junsheng,Yu Dejie,Yang Yu.The application of energy operator demodulation approach based on EMD in machinery fault diagnosis[J].Mechanical Systems and Signal Processing,2007,21(1):668-677.
[4]程军圣,杨怡,杨宇.基于LMD的能量算子解调机械故障诊断方法[J].振动、测试与诊断,2012,32(6):915-919.Cheng Junsheng,Yang Yi,Yang Yu.Machanical fault diagnosis approach based on LMD energy operator demodulating[J].Journal of Vibration,Measurement&Diagnosis,2012,32(6):915-919.(in Chinese)
[5]胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动、测试与诊断,2011,31(4):429-434.Hu Aijun,Sun Jingjing,Xiang Ling.Mode mixing in empirical mode decomposition[J].Journal of Vibration,Measurement&Diagnosis,2011,31(4):429-434.(in Chinese)
[6]张梅军,陈灏,曹勤,等.基于SVM信号延拓改进的EEMD方法[J].振动、测试与诊断,2013,33(1):93-98.Zhang Meijun,Chen Hao,Cao Qing,et al.Ensemble empirical mode decomposition method using SVM signal data extending[J].Journal of Vibration,Measurement&Diagnosis,2013,33(1):93-98.(in Chinese)
[7]Jonathan S S.The local mean decomposition and its application to EEG perception data[J].Journal of the Royal Society Interface,2005,2(5):443-454.
[8]Cheng Junsheng,Yang Yi,Yang Yu.A rotating machineryfault diagnosis method based on local mean decomposition[J].Digital Signal Processing,2012,22(2):356-366.
[9]程军圣,郑近德,杨宇.一种新的非平稳信号分析方法—局部特征尺度分解法[J].振动工程学报,2012,25(2):215-220.Cheng Junsheng,Zheng Jinde,Yang Yu.A new nonstationary signal analysis approach-the local characteristic-scale decomposition method[J].Journal of Vibration Engineering,2012,25(2):215-220.(in Chinese)
[10]秦毅,秦树人,毛永芳.基于小波脊线的解调方法及其在旋转机械故障诊断中的应用[J].机械工程学报,2009,45(2):231-237.Qin Yi,Qin Shuren,Mao Yongfang.Demodulation approach based on wavelet ridge and its application in fault diagnosis of rotating machinery[J].Chinese Journal of Mechanical Engineering,2009,45(2):231-237.(in Chinese)
[11]Zhang Xinming,He Yongyong,Hao Rujiang,et al.parameters optimization of continuous wavelet transform and its application in acoustic emission signal analysis of rolling bearing[J].Chinese Journal of Mechanical Engineering,2007,20(2):104-106.
[12]朱洪俊,王忠,秦树人.小波变换对瞬态信号特征信息的精确提取[J].机械工程学报,2005,41(12):196-199.Zhu Hongjun,Wang Zhong,Qin Shuren.Accurate extraction for the characteristic information of transient signal with wavelet transforms[J].Chinese Journal of Mechanical Engineering,2005,41(12):196-199.(in Chinese)
[13]Jiang Yonghua,Tang Baoping,Qin Yi,et al.Feature extraction method of wind turbin based on adaptive Morlet wavelet and SVD[J].Renewable Energy,2011,36(8):2146-2153.