本文選題：特征提取 + 威布爾分布��； 參考：《湖南工業(yè)大學》2011年碩士論文

【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)直接影響到整個機械系統(tǒng)的性能,對其進行故障診斷具有重要的實際意義。 基于振動信號分析的滾動軸承特征提取方法是國內(nèi)外使用最多、也是最有效的方法之一。統(tǒng)計分布模型參數(shù)在可靠性工程中已被廣泛應用于反映機械產(chǎn)品的疲勞壽命和疲勞強度,但在機械特別是軸承狀態(tài)監(jiān)測和故障診斷中用于特征提取的研究尚未多見。為了充分挖掘滾動軸承運行中蘊含的有效狀態(tài)變化信息,提出了一種基于威布爾分布模型參數(shù)及其數(shù)字特征的故障特征提取新方法。在對滾動軸承原始振動信號建立Weibull分布模型的基礎上,分別提取模型的尺度參數(shù)以及中位數(shù)作為表征軸承運行狀態(tài)的一種新特征向量。仿真試驗結(jié)果證明了該特征提取方法的有效性。 針對滾動軸承振動信號的非高斯特性,提出了一種基于對數(shù)正態(tài)分布模型的故障特征提取新思路,提取其模型參數(shù)的對數(shù)均值作為表征滾動軸承運行狀態(tài)的新特征量。有效地解決了振動信號的非高斯問題。 針對上述方法無法全面準確描述滾動軸承振動信號的非平穩(wěn)特性問題,提出了一種基于小波域?qū)?shù)正態(tài)模型的滾動軸承故障特征提取新方法。首先,對滾動軸承振動信號進行小波、小波包分析,將非平穩(wěn)信號轉(zhuǎn)化為平穩(wěn)信號,在平穩(wěn)信號的基礎上建立典型的非高斯分布模型—對數(shù)正態(tài)分布模型,最后提取每個尺度上的對數(shù)正態(tài)分布模型參數(shù)作為表征軸承運行狀態(tài)的新特征量。試驗證明了所提特征提取方法有效地解決了滾動軸承振動信號的非平穩(wěn)、非高斯問題。
[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 the whole mechanical system, so it is of great practical significance to diagnose its faults. Feature extraction of rolling bearings based on vibration signal analysis is one of the most widely used and effective methods at home and abroad. Statistical distribution model parameters have been widely used in reliability engineering to reflect fatigue life and fatigue strength of mechanical products. However, the research on feature extraction in mechanical, especially bearing condition monitoring and fault diagnosis has not been widely seen. A new fault feature extraction method based on Weibull distribution model parameters and its digital features is proposed in order to fully excavate the effective state change information contained in rolling bearing operation. Based on the Weibull distribution model of the original vibration signal of the rolling bearing, the scale parameters and the median of the model are extracted as a new characteristic vector to characterize the running state of the bearing. Simulation results show the effectiveness of the feature extraction method. Aiming at the non- characteristic of rolling bearing vibration signal, a new idea of fault feature extraction based on logarithmic normal distribution model is proposed, and the logarithmic mean of model parameters is extracted as a new characteristic quantity to characterize rolling bearing running state. The problem of non-Gao Si vibration signal is solved effectively. In view of the problem that the above methods can not accurately describe the non-stationary characteristics of rolling bearing vibration signals, a new method for fault feature extraction of rolling bearings based on wavelet domain logarithmic normal model is proposed. Firstly, the vibration signal of rolling bearing is analyzed by wavelet and wavelet packet, and the non-stationary signal is transformed into stationary signal. On the basis of stationary signal, a typical non- distribution model, logarithmic normal distribution model, is established. Finally, the parameters of the logarithmic normal distribution model on each scale are extracted as the new characteristic variables to characterize the running state of the bearing. The experimental results show that the proposed feature extraction method can effectively solve the non-stationary and non- problem of rolling bearing vibration signal.
【學位授予單位】：湖南工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關碩士學位論文 前1條

1 王善鵬;基于流形學習的滾動軸承故障特征提取方法研究[D];大連理工大學;2013年

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本文編號：1821850