基于非平稳时序分析的滚动轴承故障特征提取方法研究
[Abstract]:Rolling bearing is one of the most widely used and easily damaged parts in rotating machinery. Its working state directly affects the performance of rotating machinery system. Because the vibration signal generated by rolling bearing is a typical non-stationary random signal, it is the key to fault diagnosis to extract the fault characteristics from the vibration signal which can accurately reflect the running state of the bearing. The autoregressive (AR) parameter model is the most basic and widely used time series model in time series analysis, but the AR model is based on the assumption of stochastic stationarity and can not accurately analyze the non-stationary random signals of rolling bearings. In this paper, a fault feature extraction method based on empirical mode decomposition (EMD) and AR model is proposed. In this method, the vibration signal of rolling bearing is decomposed into several intrinsic modal functions by EMD decomposition. The first five IMF components are extracted by correlation analysis to establish AR model. The singular values of the parameters of the model and the variance of the residuals are extracted as the eigenvectors to reflect the running state of the rolling bearings. The experimental results show that this method has high accuracy and good effect. In order to overcome the problem that the precision of EMD decomposition signal is not high, this paper uses wavelet packet decomposition (WPD) to have good time-frequency localization and multi-resolution characteristics, and converts the non-stationary vibration signal into stationary signal. Based on wavelet packet autoregressive (WPD_AR) parameter model and wavelet packet time-varying autoregressive (WPD_TVAR) parameter model, two fault feature extraction methods for rolling bearing are proposed. Firstly, the vibration signal of rolling bearing is decomposed by wavelet packet, then AR model and TVAR model are established for each node coefficient obtained by decomposition. The singular values of the WPD_AR model and the WPD_TVAR model are extracted as the characteristic vectors to reflect the running state of the rolling bearing. The experimental results show that the effect of fault feature extraction by WPD-AR model is more obvious than that by EMD_AR model, and the speed of fault feature extraction by WPD-VAR model is more accurate and the accuracy is higher than that by WPD_AR model. The fault features obtained by the three fault feature extraction methods are sent to the support vector machine classifier for fault classification and diagnosis. Experiments show that the proposed method can effectively and accurately identify the fault status of rolling bearings and verify the effectiveness of the fault feature extraction method proposed in this paper based on non-stationary time series analysis.
【学位授予单位】:中南大学
【学位级别】:硕士
【学位授予年份】:2012
【分类号】:TH165.3
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