基于非平穩(wěn)時序分析的滾動軸承故障特征提取方法研究
發(fā)布時間：2018-08-05 20:06
【摘要】：滾動軸承是旋轉(zhuǎn)機械中使用最為廣泛和最易受損的零部件之一,其工作狀態(tài)直接影響到旋轉(zhuǎn)機械系統(tǒng)的性能,對其進行故障診斷具有重要的實際應(yīng)用意義。由于滾動軸承運行時產(chǎn)生的振動信號是典型的非平穩(wěn)隨機信號,從振動信號中提取能準確反映軸承運行狀態(tài)的故障特征是故障診斷的關(guān)鍵。 自回歸(AR)參數(shù)模型是時序分析方法中最基本、實際應(yīng)用最廣的時序模型,但AR模型分析信號是建立在隨機平穩(wěn)性的假設(shè)基礎(chǔ)上,無法準確分析滾動軸承非平穩(wěn)隨機信號。為此,論文提出一種基于經(jīng)驗?zāi)B(tài)分解(EMD)和AR模型相結(jié)合的滾動軸承故障特征提取方法。該方法用EMD分解將滾動軸承振動信號分解為若干內(nèi)稟模態(tài)函數(shù)(IMF),采用相關(guān)分析提取前五個IMF分量建立AR模型,提取模型的參數(shù)和殘差的方差的奇異值作為反映滾動軸承運行狀態(tài)的特征向量。實驗結(jié)果表明該方法提取特征精度較高,效果較好。 為克服EMD分解信號精度不高的問題,論文利用小波包分解(WPD)具有良好的時頻局部化特性和多分辨率的特征,將非平穩(wěn)振動信號轉(zhuǎn)化為平穩(wěn)信號的基礎(chǔ)上,把振動信號分解到各個頻段中使AR模型能有效跟蹤信號,提出了基于小波包自回歸(WPD_AR)參數(shù)模型和小波包時變自回歸(WPD_TVAR)參數(shù)模型的兩種滾動軸承故障特征提取方法。首先對滾動軸承振動信號進行小波包分解,然后對分解得到的各結(jié)點系數(shù)分別建立AR模型和TVAR模型,分別提取WPD_AR模型和WPD_TVAR模型的參數(shù)奇異值作為反映滾動軸承運行狀態(tài)的特征向量。實驗結(jié)果表明,WPD_AR模型比EMD_AR模型故障特征提取的效果更明顯,速度更快；WPD_TVAR模型比WPD_AR模型故障特征提取的結(jié)果更準確,精度更高。 將提出的三種故障特征提取方法獲取的故障特征送入支持向量機分類器進行故障分類與診斷。實驗表明,本文所提方法可以有效、準確地識別滾動軸承的故障狀況,驗證了論文提出的基于非平穩(wěn)時序分析的滾動軸承故障特征提取方法的有效性。
[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.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3
本文編號：2166876
[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.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3
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