n-FFT压缩感知降维智能方法及其在齿轮系统故障特征提取与分类中应用研究
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摘要
机械传动被广泛应用在车辆、机床、工程机械等工程多个领域,因此研究机械传动的可靠性、损伤机理和故障诊断与信号处理方法具有重要意义和价值。传统的动态信号处理如FFT算法,即快速傅里叶变换法,因其运算速度快,谱分析特征稳定,所以得到了广泛的工程应用。但是工程实际中信号往往是非平稳的时变的非线性,非常复杂,再加之内外界噪声的干扰,分析处理较为困难,从而导致以FFT分析为主导的谱分析方法受到了很大限制。利用FFT运算速度快,特征稳定的优点,提出了对二次FFT,三次FFT算法,n次FFT算法(2n小于信号样本点数)进行探讨和研究的思想,从理论上进行探讨、并通过实验进行了验证,并将其应用于齿轮运行状态的故障特征监测和分类研究之中。分析研究表明n-FFT算法运行速度快,特征积聚度高,有利于进行齿轮运行状态的分类和故障监测与诊断。
Mechanical transmission is widely used in vehicles,machine tools,construction machinery and other engineering fields,the study of mechanical transmission reliability,damage mechanism,fault diagnosis and signal processing method has great significance and value.The traditional signal processing algorithms,such as FFT algorithm,the fast Fourier transform algorithm,since its fast speed,stable spectrum analysis,has been widely used in engineering. But the signals of engineering practice are very complex,nonlinear,and non-stationary,Within the external noise interference coupled,makes the analysis and processing more difficult,which lead to the spectral analysis based on FFT method has been very limited. For the advantages of computation speed,features and stability of FFT algorithm,the secondary FFT,third FFT algorithm,n-FFT algorithm( 2 npoints less than the signal sample points) were discussed in theory and research,the theory was explored and verified by experiment signals. It was applied in the classification study of gear signal. Analysis shows that n-FFT algorithm is fast,with high feature accumulation,is beneficial to the classify and fault monitoring and diagnosis of gear operating state.
Mechanical transmission is widely used in vehicles,machine tools,construction machinery and other engineering fields,the study of mechanical transmission reliability,damage mechanism,fault diagnosis and signal processing method has great significance and value.The traditional signal processing algorithms,such as FFT algorithm,the fast Fourier transform algorithm,since its fast speed,stable spectrum analysis,has been widely used in engineering. But the signals of engineering practice are very complex,nonlinear,and non-stationary,Within the external noise interference coupled,makes the analysis and processing more difficult,which lead to the spectral analysis based on FFT method has been very limited. For the advantages of computation speed,features and stability of FFT algorithm,the secondary FFT,third FFT algorithm,n-FFT algorithm( 2 npoints less than the signal sample points) were discussed in theory and research,the theory was explored and verified by experiment signals. It was applied in the classification study of gear signal. Analysis shows that n-FFT algorithm is fast,with high feature accumulation,is beneficial to the classify and fault monitoring and diagnosis of gear operating state.
引文
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[19]A.Santhana Raj;N.Murali.Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference[J].IEEETransactions on Industrial Electronics,2013.60(2):567-574.
[20]冯辅周,司爱威,江鹏程.小波相关排列熵和HMM在故障预测中的应用[J].振动工程学报,2013,26(2):269-276.FENG FuZhou,SI Ai Wei,JIANG PengCheng.Application of wavelet correlation permutation entropy and hidden Markov model to fault prognostic[J].Journal of Vibration Engineering,2013,26(2):269-276(In Chinese).
[21]Xu H.,Chen G.An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO[J].Mechanical Systems and Signal Processing,2013,35(1-2):167-175.
[22]Valian E.,Tavakoli S.,Mohanna S.,et al.Improved cuckoo search for reliability optimization problems[J].Computers&Industrial Engineering,2013,64(1):459-468.
[2]唐贵基,邓飞跃,何玉灵,等.基于时间-小波能量谱熵的滚动轴承故障诊断研究[J].振动与冲击,2014,33(7):68-72.TANG GuiJi,DENG Fei Yue,HE Yu Ling,et al.Rolling element bearing fault diagnosis based on time-wavelet energy spectrum entropy[J].Journal of Vibration and Shock,2014,33(7):68-72(In Chinese).
[3]李宏坤,练晓婷,周帅.小波尺度谱同步平均在弱信息识别中的应用研究[J].机械工程学报,2013,49(5):32-38.LI Hong Kun,LIAN Xiao Ting,ZHOU Shuai.Application on weak information classification by using wavelet scalogram synchronous averaging[J].Journal of Mechanical Engineering,2013,49(5):32-38(In Chinese).
[4]Shao R,Dong F,Wang W,et al.Influence of cracks on dynamic characteristics and stress intensity factor of gears[J].Engineering Failure Analysis,2013(32):63-80.
[5]胥永刚,马海龙,付胜,等.机电设备早期故障微弱信号的非线性检测方法及工程应用[J].振动工程学报,2011,24(5):529-537.XU YongGang,MAI Hai Long,FU Sheng,et al.Theory and a pplications of weak signal non-linear detection method for incipient fault diagnosis of mechanical equipments[J].Journal of Vibration Engineering,2011,24(5):529-537(In Chinese).
[6]Ruqiang Yan,Robert X.Gao.Harmonic wavelet-based data filtering for enhanced machine defect identification[J].Journal of Sound and Vibration,2010,329(15):3203-3217.
[7]雷亚国,韩冬,林京,等.自适应随机共振新方法及其在故障诊断中的应用[J].机械工程学报,2012,48(7):62-67.LEI Ya Guo,HAN Dong,LIN Jing,et al.New adaptive stochastic resonance method and its application to fault diagnosis[J].Journal of Mechanical Engineering,2012,48(7):62-67(In Chinese).
[8]Shao R,Hu W,Cao J.Gear damage detection and diagnosis system based on COM module[J].Procedia Engineering,2011(15):2301-2307.
[9]Shao R,Hu W,Wang Y,et al.The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform[J].Measurement,2014,54(6):118-132.
[10]丁康,黄志东,林慧斌.一种谱峭度和Morlet小波的滚动轴承微弱故障诊断方法[J].振动工程学报,2014,27(1):128-134.DING Kang,HUANG ZhiDong,LIN HuiBin.A weak fault diagnosis method for rolling element bearing based on Morlet wavelet and spectral kurtosis[J].Journal of Vibration Engineering,2014,27(1):128-134(In Chinese).
[11]杨绍普,赵志宏.改进的小波相邻系数降噪方法及其在机械故障诊断中的应用[J].机械工程学报,2013,49(17):131-137.YANG ShaoPu,ZHAO ZhiHong.Improved wavelet denoising using neighboring coefficients and its application to machinery fault diagnosis[J].Journal of Mechanical Engineering,2013,49(17):131-137(In Chinese).
[12]王红军,万鹏.基于EEMD和小波包变换的早期故障敏感特征获取[J].北京理工大学学报,2013,33(9):945-950.WANG Hong Jun,WAN Peng.Sensitive features extraction of early fault based on EEMD and WPT[J].Transactions of Beijing Institute of Technology,2013,33(9):945-950(In Chinese).
[13]田福庆,罗荣,李万,等.改进的谐波小波包峭度图及其应用[J].上海交通大学学报,2014,48(1):39-44.TIAN FuQing,LUO Rong,LI Wan,et al.Improved harmonic wavelet packet kurtogram and its application[J].Journal of Shangai Jiaotong University,2014,48(1):39-44(In Chinese).
[14]Yabin Dong,Mingfu Liao,Xiaolong Zhang,et al.Faults diagnosis of rolling element bearings based on modified morphological method[J].Mechanical Systems and Signal Processing,2011,25(4):1276-1286.
[15]Newland D E.Harmonic wavelet analysis[C]//Proceedings of the Royal Society of London.A,1993,43(10):203-225.
[16]Newland D E.Wavelet analysis of vibration-part 1:theory[J].Journal Vibration and Acoustics,Transactions of the ASME,1994,116(10):409-416.
[17]Dwyer R F.Detection of non-Gaussian signals by frequency domain kurtosis estimation[A].Proceedings of the International Conference on Acoustic,Speech,and Signal Processing[C].Boston,1983:607-610.
[18]Antoni J,Randall R B.The spectral kurtosis:application to the vibratory surveillance and diagnostics of rotating machines[J].Mechanical System and Signal Processing,2006,20(2):308-331.
[19]A.Santhana Raj;N.Murali.Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference[J].IEEETransactions on Industrial Electronics,2013.60(2):567-574.
[20]冯辅周,司爱威,江鹏程.小波相关排列熵和HMM在故障预测中的应用[J].振动工程学报,2013,26(2):269-276.FENG FuZhou,SI Ai Wei,JIANG PengCheng.Application of wavelet correlation permutation entropy and hidden Markov model to fault prognostic[J].Journal of Vibration Engineering,2013,26(2):269-276(In Chinese).
[21]Xu H.,Chen G.An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO[J].Mechanical Systems and Signal Processing,2013,35(1-2):167-175.
[22]Valian E.,Tavakoli S.,Mohanna S.,et al.Improved cuckoo search for reliability optimization problems[J].Computers&Industrial Engineering,2013,64(1):459-468.