最优共振带提取与1.5维谱的滚动轴承早期故障诊断方法
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摘要
滚动轴承的振动信号反映到频谱图中,会出现共振带,能够有效并准确提取共振带加以分析是滚动轴承故障诊断常用方法。为了准确提取出共振带,采用巴特沃斯带通滤波器对共振频带进行提取,为了得到最优共振带,将采用特征频率强度系数这一指标来反映提取的共振带效果,然后利用具有高强降噪特性的1.5维谱来对滤波信号进行特征提取.通过仿真信号以及试验信号对该方法进行验证,结果表明,该方法能够在强噪背景下对特征的提取以及实现滚动轴承早期故障诊断。
The vibration signals of the rolling bearing are reflected in the spectrum,and there will be resonance bands. It is a common method for the fault diagnosis of rolling bearing to analyze the resonance band effectively and accurately. In order to extract the resonance band accurately,the Butterworth filter is used to extract the resonance band. In order to obtain the optimal resonance band,the characteristic frequency strength coefficient is used to reflect the effect of the extracted resonance band. Then the 1.5 dimensional spectrum with high strength noise reduction characteristic is used to feature the filtered signal.The method is verified by the simulation signal and the test signal. The result shows that the method can extract the features in the strong noise background and realize the early fault diagnosis of the rolling bearing.
The vibration signals of the rolling bearing are reflected in the spectrum,and there will be resonance bands. It is a common method for the fault diagnosis of rolling bearing to analyze the resonance band effectively and accurately. In order to extract the resonance band accurately,the Butterworth filter is used to extract the resonance band. In order to obtain the optimal resonance band,the characteristic frequency strength coefficient is used to reflect the effect of the extracted resonance band. Then the 1.5 dimensional spectrum with high strength noise reduction characteristic is used to feature the filtered signal.The method is verified by the simulation signal and the test signal. The result shows that the method can extract the features in the strong noise background and realize the early fault diagnosis of the rolling bearing.
引文
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[10]Chen L,Yanyang Z I,Zhengjia H E.Research and application of ensemble empirical mode decomposition principle and 1.5 dimension spectrum method[J].Journal of Xian Jiaotong University,2009,43(5):94-98.
[2]Fan W,Li Y,Tsui K L.A noise resistant correlation method for period detection of noisy signals[J].IEEE Transactions on Signal Processing,2018,PP(99):1-1.
[3]Wang D,Tsui K L.Dynamic Bayesian wavelet transform:New methodology for extraction of repetitive transients[J].Mechanical Systems&Signal Processing,2017(88):137-144.
[4]叶昊,王桂增,方崇智.小波变换在故障检测中的应用[J].自动化学报,1997,23(6):736-741.(Ye Hao,Wang Gui-zeng,Fang Chong-zhi.Application of wavelet transform in fault detection[J].Journal of Automation,1997,23(6):736-741.)
[5]Lei Y,He Z,Zi Y.Application of the EEMD method to rotor fault diagnosis of rotating machinery[J].Mechanical Systems&Signal Processing,2009,23(4):1327-1338.
[6]Huang N E,Shen Z,Long S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings Mathematical Physical&Engineering Sciences,1998,454(1971):903-995.
[7]周智,朱永生,张优云.基于EEMD和共振解调的滚动轴承自适应故障诊断[J].振动与冲击,2013,32(2):76-80.(Zhou Zhi,Zhu Yong-sheng,Zhang You-yun.Adaptive fault diagnosis of rolling bearings based on EEMD and resonance demodulation[J].Vibration and Shock,2013,32(2):76-80.)
[8]桑迎平,蔡晋辉,曾九孙.基于形态优化滤波的轴承故障特征提取方法[J].电子测量与仪器学报,2016,30(5):757-763.(Sang Ying-ping,Cai Jin-hui,Zeng jiu-sun.Feature extraction method of bearing fault based on morphological optimization filtering[J].Journal of Electronic Measurement and Instrumentation,2016,30(5):757-763.)
[9]王晓龙,唐贵基.一种基于连续小波变换的滚动轴承早期故障诊断新方法[J].推进技术,2016,37(8):1431-1437.(Wang Xiao-long,Tang Gui-ji.A new method for early fault diagnosis of rolling bearings based on continuous wavelet transform[J].Propulsion Technology,2016,37(8):1431-1437.)
[10]Chen L,Yanyang Z I,Zhengjia H E.Research and application of ensemble empirical mode decomposition principle and 1.5 dimension spectrum method[J].Journal of Xian Jiaotong University,2009,43(5):94-98.