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| The Fault Diagnosis of Rotating Machine Based on Blind Source Separation De-noising and Hilbert-Huang Transform |
| MENG Zong,GU Hai-yan |
| Yanshan University, Key Laboratory of Measurement Technology and Instrumentation of Hebei Province,Qinhuangdao, Hebei 066004, China |
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Abstract In the characteristics extraction of the rotating machine with Hilbert-Huang transform, the vibration signals from the sensors mounted on the machine are generally suffered by the disturbance from different types of noise.The neglect of the noise generally causes worse effect of analysis. In order to overcome this deficiency,by means of combining with the blind source separation,a new algorithm,which is named Hilbert-Huang transform based on the fast independent component analysis,is proposed to resolve the mode fission.The simulation and case analysis show that the proposed method is very effective to waken the phenomenon, and to extract the characteristic frequency of the signal,further to realize the fault diagnosis of rotating machinery.
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［1］Huang N E, Shen Z, Long S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis［J］. Proceeding of Royal Society London,1998,454(1971): 903-995.
［2］谢平,王欢,杜义浩.基于EMD和Wigner-Ville分布的机械故障诊断方法研究［J］.计量学报,2010,31(5):390-394.
［3］周鹏,秦树人.多分辨Hilbert边际谱在旋转机械故障诊断中的应用［J］.计量技术,2007,（2）:72-74.
［4］曹冲锋,杨世锡,杨将新.大型旋转机械非平稳振动信号的EEMD降噪方法［J］.振动与冲击,2009,28(9):33-38.
［5］Huang W,Shen Z, Norden E, et al. Nonlinear indicial response of complex nonstationary oscillations as pulmonary hypertension responding to step hypoxia［J］.Proc Natl Acad Sci USA,1999,96(5):1834-1839.
［6］赵进平.异常事件对EMD方法的影响及其解决方法研究［J］.青岛海洋大学学报,2001,31(6):805-814.
［7］唐贵基,王誉蓉,胡爱军,等.基于第二代小波变换的旋转机械振动信号去噪［J］.噪声与振动控制,2007,27(1):51-53.
［8］Jutten C, Herault J.Blind separation of sources, Part I: an adaptive algorithm based on neuromimetic architecture［J］.Signal Processing,1991,24(1):1－10.
［9］孙世军,彭承琳,侯文生,等.基于时间序列变分贝叶斯理论的信号盲源分离［J］.仪器仪表学报,2009,30(8):1571-1576.
［10］Hyvrinen A, Oja E. Independent component analysis: algorithms and applications［J］.Neural Netw,2000,13(4－5): 411-430. |
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2013, Vol. 34 
