基于改进的经验小波变换的转子故障信号处理研究
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摘要
鉴于转子故障振动信号成分复杂,以及信号采集难免会存在一些干扰信号,为排除干扰信号以及非主要成分,提出了改进的经验小波变换信号处理。它对采集的信号进行经验小波变换,求取变换后各频带的相关系数,去除相关系数较小的频带,从而去除信号中非主要特征及干扰信号,获得只含主要特征的信号。通过具体实验,该方法有效地提高了信号的真实性。最终将其应用于机械转子故障中得到良好的效果。
The components of the rotor fault vibration signal are complex, and there are some interference signals in the signal acquisition. To eliminate the interference signals, this paper presents an improved empirical wavelet transform signal processing. It performs empirical wavelet transformation of the acquired signal, obtains the correlation coefficient of each frequency band after the transformation, and removes the frequency band with a small correlation coefficient, thereby removing the non-primary features and interference signals in the signal and obtaining the signal with only the main features. With the specific experiments, this method effectively improves the authenticity of the signal. Finally, it is applied to mechanical rotor failure to get good results.
The components of the rotor fault vibration signal are complex, and there are some interference signals in the signal acquisition. To eliminate the interference signals, this paper presents an improved empirical wavelet transform signal processing. It performs empirical wavelet transformation of the acquired signal, obtains the correlation coefficient of each frequency band after the transformation, and removes the frequency band with a small correlation coefficient, thereby removing the non-primary features and interference signals in the signal and obtaining the signal with only the main features. With the specific experiments, this method effectively improves the authenticity of the signal. Finally, it is applied to mechanical rotor failure to get good results.
引文
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[6]刘晓东,刘朦月,陈寅生,等.EEMD-PE与M-RVM相结合的轴承故障诊断方法[J].哈尔滨工业大学学报,2017,46(9):122-128.
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[9]陈志新,刘鑫,卢成林,等.基于经验小波变换的复杂强噪声背景下弱故障检测方法[J].农业工程学报,2016,32(20):202-208.
[10]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014(11):2423-2432.
[11]朱艳萍,包文杰,涂晓彤,等.改进的经验小波变换在滚动轴承故障诊断中的应用[J].噪声与振动控制,2018,38(1):199-203.
[12]丁浩,赵建昕,笪良龙.一种通用型基于经验模态分解的小波阈值滤波方法研究[J].舰船科学技术,2016,38(13):71-76.
[13]王莹.基于成分分解的自适应滤波降噪方法研究[D].哈尔滨:哈尔滨工业大学,2017.
[14]贾俊平,何晓群,金勇进.统计学[M].5版.北京:中国人民大学出版社,2014:265-273.
[15]孙廷凯.增强型典型相关分析研究与应用[D].南京:南京航空航天大学,2006.
[2]LI H,ZHENG H,TANG L.Wigner-Ville Distribution Based on EMD for Faults Diagnosis of Bearing[M]//Fuzzy Systems and Knowledge Discovery.Springer Berlin Heidelberg,2006:803-812.
[3]李辉,郑海起,唐力伟.基于EEMD和THT的齿轮故障诊断方法[J].振动、测试与诊断,2011,31(4):496-500.
[4]HUANG N E,WU M C,LONG S R,et al.A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,2003,459(2037):2317-2345.
[5]武哲,杨绍普,刘永强.基于多元经验模态分解的旋转机械早期故障诊断方法[J].仪器仪表学报,2016,37(2):241-248.
[6]刘晓东,刘朦月,陈寅生,等.EEMD-PE与M-RVM相结合的轴承故障诊断方法[J].哈尔滨工业大学学报,2017,46(9):122-128.
[7]GILLES J.Empirical Wavelet Transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[8]GAO Z,LIN J,WANG X,et al.Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission[J].Materials,2017,10(6):571.
[9]陈志新,刘鑫,卢成林,等.基于经验小波变换的复杂强噪声背景下弱故障检测方法[J].农业工程学报,2016,32(20):202-208.
[10]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014(11):2423-2432.
[11]朱艳萍,包文杰,涂晓彤,等.改进的经验小波变换在滚动轴承故障诊断中的应用[J].噪声与振动控制,2018,38(1):199-203.
[12]丁浩,赵建昕,笪良龙.一种通用型基于经验模态分解的小波阈值滤波方法研究[J].舰船科学技术,2016,38(13):71-76.
[13]王莹.基于成分分解的自适应滤波降噪方法研究[D].哈尔滨:哈尔滨工业大学,2017.
[14]贾俊平,何晓群,金勇进.统计学[M].5版.北京:中国人民大学出版社,2014:265-273.
[15]孙廷凯.增强型典型相关分析研究与应用[D].南京:南京航空航天大学,2006.