旋轉(zhuǎn)機械轉(zhuǎn)子—滾動軸承復合故障診斷方法研究
發(fā)布時間：2019-03-27 10:51
【摘要】：旋轉(zhuǎn)機械轉(zhuǎn)子-滾動軸承系統(tǒng)中,復合故障發(fā)生的概率大于單一故障,且發(fā)生復合故障時,準確識別缺陷類型,以便提早安排維修計劃,防止意外停機是關(guān)鍵,所以研究復合故障診斷更有意義。復合故障診斷方法,除了能夠診斷出復合故障的情況外,還應向下兼容,具備診斷單一故障的能力。本文通過試驗探索了轉(zhuǎn)子不平衡、不對中和滾動軸承6種復合故障的振動特征,研究發(fā)現(xiàn):復合故障振動信號含有各單一故障的特征信息,同時也存在耦合的特征信息,而且復合故障對單一故障特征有很大影響。在軸承座上拾取的振動信號十分復雜,但總體可概括為滾動故障信號、確定性信號和噪聲,微弱故障信號可能被其他信號掩蓋,難以提取故障特征。針對這一問題文中提出了基于LMD-MED的故障特征提取方法,LMD作為MED的預處理方法,MED處理前4個PF分量PF分量,來消除噪聲和增強微弱沖擊信號。文中通過SKF6205滾動軸承滾動體早期缺陷試驗數(shù)據(jù),證明該方法有效提取出了故障特征,并且峰值占比和信噪比較傳統(tǒng)方法分別增加了150%和18.3%。針對LMD-MED方法無法直接確定MED步長和PF分量的問題,提出一種基于峭度-步長及信噪比-分量選取的改進LMD-MED方法。首先,搜索LMD-MED處理后的峭度拐點,以此確定MED步長;然后,在Hilbert包絡(luò)信號中分別計算滾動軸承故障頻率處的信噪比,確定最大信噪比對應的PF分量和故障類型;最后,對選定分量作Hilbert包絡(luò)分析。文中通過試驗,說明了該方法在單一滾動軸承故障診斷及復合故障中滾動軸承故障診斷方面都有良好效果。利用這些試驗數(shù)據(jù),將EMD-MED方法和LMD-MED方法進行對比研究,結(jié)果發(fā)現(xiàn):LMD的分量能量較EMD集中,模式混疊較輕,同時LMD將部分高頻噪聲分解到了目標分量中;而EMD卻可以分解出目標分量中的大部分噪聲,信噪比較高。為構(gòu)建復合故障特征集,分別在信號的低頻段和滾動軸承一階共振區(qū)頻段提取時頻域特征。文中利用小波包分解對信號進行分解和重構(gòu),重構(gòu)低頻段信號,提取2個頻域特征和8個時域特征;重構(gòu)滾動軸承一階共振區(qū)信號,提取EMD處理后IMF1分量的6個頻域特征和8個時域特征;將這些特征組成復合故障特征集。在訓練集中,采用kPCA提取該特征集的核主元,利用PSO方法優(yōu)化SVM懲罰參數(shù)和RBF核函數(shù)參數(shù),訓練SVM模型。文中通過轉(zhuǎn)子不平衡、不對中和N205滾動軸承12種故障試驗證實了該方法的有效性,訓練集3折交叉驗證分類精度和測試集分類精度分別達到99.44%和99.58%,故障識別精度較高。
[Abstract]:In the rotary mechanical rotor-rolling bearing system, the probability of the composite fault occurrence is greater than a single fault, and when the composite fault occurs, the type of the defect is accurately identified, so that the maintenance plan can be arranged early so as to prevent the accidental shutdown from being critical, so the research of the composite fault diagnosis is more meaningful. The composite fault diagnosis method is compatible with the ability to diagnose a single fault, in addition to the ability to diagnose the complex fault. In this paper, the vibration characteristics of the six kinds of composite faults of the rotor unbalance, the non-centering and the rolling bearing are explored. The results show that the composite fault vibration signal contains the characteristic information of each single fault, and the characteristic information of the coupling is also present. And the composite fault has a great influence on the single fault characteristic. The vibration signal picked up on the bearing seat is very complex, but can be generally summarized as a rolling fault signal, a deterministic signal and a noise, and the weak fault signal may be masked by other signals, making it difficult to extract the fault features. In this paper, a fault feature extraction method based on LMD-MED is proposed. LMD is used as the pre-processing method of MED. The PF component of four PF components before MED process is used to eliminate the noise and enhance the weak impact signal. In this paper, the early-defect test data of the rolling element of the rolling bearing of the SKF6205 rolling bearing proves that the method can effectively extract the fault characteristic, and the peak-to-noise ratio and the signal-to-noise ratio are increased by 150% and 18.3%, respectively. An improved LMD-MED method is proposed for LMD-MED method which can not directly determine the MED step size and the PF component, and proposes an improved LMD-MED method based on the kurtosis-step size and the signal-to-noise ratio-component selection. Firstly, searching the inflection point of the rolling bearing after the LMD-MED processing to determine the MED step size; then, respectively calculating the signal-to-noise ratio at the fault frequency of the rolling bearing in a Hilbert envelope signal, and determining the PF component and the fault type corresponding to the maximum signal-to-noise ratio; and finally, performing a Hilbert envelope analysis on the selected component. The test shows that the method has good effect in the fault diagnosis of single rolling bearing and the fault diagnosis of rolling bearing. Using these test data, the EMD-MED method and the LMD-MED method are compared and studied. The result shows that the component energy of the LMD is concentrated in the EMD, the mode aliasing is light, and the LMD decomposes part of the high-frequency noise into the target component, and the EMD can decompose most of the noise in the target component, The signal-to-noise ratio is high. In ord to construct a composite fault feature set, that frequency domain characteristic is respectively extracted at the low frequency section of the signal and the first order resonance region frequency band of the rolling bearing. In this paper, the wavelet packet decomposition is used to decompose and reconstruct the signal, the low-frequency segment signal is reconstructed, the two frequency-domain characteristics and the 8 time-domain characteristics are extracted, the first-order resonance region signal of the rolling bearing is reconstructed, and the six frequency-domain characteristics and the 8 time-domain characteristics of the IMF1 component after the EMD processing are extracted; These features are combined into a composite fault feature set. In the training set, the kernel principal component of the feature set is extracted by the kPCA, and the SVM model is trained by using the PSO method to optimize the SVM penalty parameter and the RBF kernel function parameter. In this paper, the validity of this method is confirmed by the unbalance of the rotor and the 12 fault tests of the neutral and N205 rolling bearings. The accuracy of the three-fold cross-validation classification and the classification accuracy of the test set are 99.44% and 99.58%, respectively, and the fault recognition accuracy is high.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133.3
本文編號：2448116
[Abstract]:In the rotary mechanical rotor-rolling bearing system, the probability of the composite fault occurrence is greater than a single fault, and when the composite fault occurs, the type of the defect is accurately identified, so that the maintenance plan can be arranged early so as to prevent the accidental shutdown from being critical, so the research of the composite fault diagnosis is more meaningful. The composite fault diagnosis method is compatible with the ability to diagnose a single fault, in addition to the ability to diagnose the complex fault. In this paper, the vibration characteristics of the six kinds of composite faults of the rotor unbalance, the non-centering and the rolling bearing are explored. The results show that the composite fault vibration signal contains the characteristic information of each single fault, and the characteristic information of the coupling is also present. And the composite fault has a great influence on the single fault characteristic. The vibration signal picked up on the bearing seat is very complex, but can be generally summarized as a rolling fault signal, a deterministic signal and a noise, and the weak fault signal may be masked by other signals, making it difficult to extract the fault features. In this paper, a fault feature extraction method based on LMD-MED is proposed. LMD is used as the pre-processing method of MED. The PF component of four PF components before MED process is used to eliminate the noise and enhance the weak impact signal. In this paper, the early-defect test data of the rolling element of the rolling bearing of the SKF6205 rolling bearing proves that the method can effectively extract the fault characteristic, and the peak-to-noise ratio and the signal-to-noise ratio are increased by 150% and 18.3%, respectively. An improved LMD-MED method is proposed for LMD-MED method which can not directly determine the MED step size and the PF component, and proposes an improved LMD-MED method based on the kurtosis-step size and the signal-to-noise ratio-component selection. Firstly, searching the inflection point of the rolling bearing after the LMD-MED processing to determine the MED step size; then, respectively calculating the signal-to-noise ratio at the fault frequency of the rolling bearing in a Hilbert envelope signal, and determining the PF component and the fault type corresponding to the maximum signal-to-noise ratio; and finally, performing a Hilbert envelope analysis on the selected component. The test shows that the method has good effect in the fault diagnosis of single rolling bearing and the fault diagnosis of rolling bearing. Using these test data, the EMD-MED method and the LMD-MED method are compared and studied. The result shows that the component energy of the LMD is concentrated in the EMD, the mode aliasing is light, and the LMD decomposes part of the high-frequency noise into the target component, and the EMD can decompose most of the noise in the target component, The signal-to-noise ratio is high. In ord to construct a composite fault feature set, that frequency domain characteristic is respectively extracted at the low frequency section of the signal and the first order resonance region frequency band of the rolling bearing. In this paper, the wavelet packet decomposition is used to decompose and reconstruct the signal, the low-frequency segment signal is reconstructed, the two frequency-domain characteristics and the 8 time-domain characteristics are extracted, the first-order resonance region signal of the rolling bearing is reconstructed, and the six frequency-domain characteristics and the 8 time-domain characteristics of the IMF1 component after the EMD processing are extracted; These features are combined into a composite fault feature set. In the training set, the kernel principal component of the feature set is extracted by the kPCA, and the SVM model is trained by using the PSO method to optimize the SVM penalty parameter and the RBF kernel function parameter. In this paper, the validity of this method is confirmed by the unbalance of the rotor and the 12 fault tests of the neutral and N205 rolling bearings. The accuracy of the three-fold cross-validation classification and the classification accuracy of the test set are 99.44% and 99.58%, respectively, and the fault recognition accuracy is high.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133.3
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