本文選題：相空間重構 + 形態(tài)濾波�。� 參考：《武漢科技大學》2014年碩士論文

【摘要】：滾動軸承是各種旋轉機械中應用最廣泛的一種通用機械部件，它的運行狀態(tài)是否正常往往影響整臺機器的性能。因此，對滾動軸承進行故障診斷具有重要的意義。滾動軸承的故障診斷一般是對非線性時間序列表示的信號進行分析，比如特征提取、狀態(tài)識別。主要研究內容如下： (1)滾動軸承信號往往含有噪聲，為了降低噪聲對特征提取的影響，因此有必要在特征提取之前對信號作降噪處理。本文提出了基于相空間重構技術的主分量分析降噪算法，并用仿真信號和滾動軸承故障實驗數(shù)據(jù)證明了該方法在降噪方面的有效性。 (2)研究了形態(tài)濾波與基于相空間重構技術的主分量分析降噪算法相結合的特征提取算法。信號經(jīng)基于相空間重構的主分量分析降噪方法降噪之后，再用形態(tài)濾波進行特征提取。仿真研究與滾動軸承故障內圈和外圈實驗數(shù)據(jù)的實例分析，證明了該方法的有效性。 (3)研究了局部均值分解（LMD）與基于相空間重構技術的主分量分析降噪算法相結合的特征提取算法。信號基于相空間重構的主分量分析降噪方法降噪之后，再用LMD對其進行分解，選取能量最高的PF1進行包絡譜分析。通過仿真試驗和滾動軸承故障實驗，結果表明該方法能夠有效地提取出信號的故障特征。 (4)研究了多尺度排列熵與支持向量機結合的滾動軸承狀態(tài)識別算法。通過計算各個尺度下滾動軸承四種狀態(tài)信號的排列熵值，選擇合適的尺度來構建特征向量，選取一定數(shù)量的特征向量樣本并運用支持向量機分類器來對其進行分類，結果表明該方法對滾動軸承的正常、內圈故障、外圈故障、滾動體故障這四種狀態(tài)具有很高的識別率。
[Abstract]:Rolling bearing is one of the most widely used universal mechanical parts in all kinds of rotating machinery. Whether its running state is normal or not often affects the performance of the whole machine. Therefore, the rolling bearing fault diagnosis is of great significance. The fault diagnosis of rolling bearings is usually based on the analysis of nonlinear time series signals, such as feature extraction and state recognition. The main research contents are as follows: (1) Rolling bearing signals often contain noise. In order to reduce the influence of noise on feature extraction, it is necessary to do noise reduction before feature extraction. In this paper, a principal component analysis (PCA) denoising algorithm based on phase space reconstruction is proposed. Simulation signals and rolling bearing fault data are used to prove the effectiveness of this method in noise reduction. The feature extraction algorithm based on morphological filtering and principal component analysis (PCA) de-noising algorithm based on phase space reconstruction is studied. After the signal is de-noised by principal component analysis (PCA) based on phase space reconstruction, morphological filtering is used for feature extraction. Simulation study and analysis of the experimental data of the inner ring and outer ring of rolling bearing fault, The validity of this method is proved. (3) the feature extraction algorithm based on local mean decomposition (LMD) and principal component analysis (PCA) denoising algorithm based on phase space reconstruction is studied. After the noise reduction based on the principal component analysis (PCA) method of phase space reconstruction, the PF1 with the highest energy is decomposed by LMD, and the envelope spectrum is analyzed. The simulation and rolling bearing fault experiments show that the method can effectively extract the fault characteristics of the signal. (4) A rolling bearing state recognition algorithm combining multi-scale permutation entropy and support vector machine is studied. By calculating the permutation entropy values of the four state signals of rolling bearing at each scale, choosing the appropriate scale to construct the eigenvector, selecting a certain number of feature vector samples and classifying them by using support vector machine classifier. The results show that the method has a high recognition rate for the normal, inner ring, outer ring and rolling body faults of the rolling bearing.
【學位授予單位】：武漢科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TH133.33;TH165.3

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