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

【摘要】：滾動(dòng)軸承是各種旋轉(zhuǎn)機(jī)械中應(yīng)用最廣泛的一種通用機(jī)械部件，它的運(yùn)行狀態(tài)是否正常往往影響整臺(tái)機(jī)器的性能。因此，對(duì)滾動(dòng)軸承進(jìn)行故障診斷具有重要的意義。滾動(dòng)軸承的故障診斷一般是對(duì)非線性時(shí)間序列表示的信號(hào)進(jìn)行分析，比如特征提取、狀態(tài)識(shí)別。主要研究?jī)?nèi)容如下： (1)滾動(dòng)軸承信號(hào)往往含有噪聲，為了降低噪聲對(duì)特征提取的影響，因此有必要在特征提取之前對(duì)信號(hào)作降噪處理。本文提出了基于相空間重構(gòu)技術(shù)的主分量分析降噪算法，并用仿真信號(hào)和滾動(dòng)軸承故障實(shí)驗(yàn)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(gòu)技術(shù)的主分量分析降噪算法相結(jié)合的特征提取算法。信號(hào)經(jīng)基于相空間重構(gòu)的主分量分析降噪方法降噪之后，再用形態(tài)濾波進(jìn)行特征提取。仿真研究與滾動(dòng)軸承故障內(nèi)圈和外圈實(shí)驗(yàn)數(shù)據(jù)的實(shí)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(gòu)技術(shù)的主分量分析降噪算法相結(jié)合的特征提取算法。信號(hào)基于相空間重構(gòu)的主分量分析降噪方法降噪之后，再用LMD對(duì)其進(jìn)行分解，選取能量最高的PF1進(jìn)行包絡(luò)譜分析。通過(guò)仿真試驗(yàn)和滾動(dòng)軸承故障實(shí)驗(yàn)，結(jié)果表明該方法能夠有效地提取出信號(hào)的故障特征。 (4)研究了多尺度排列熵與支持向量機(jī)結(jié)合的滾動(dòng)軸承狀態(tài)識(shí)別算法。通過(guò)計(jì)算各個(gè)尺度下滾動(dòng)軸承四種狀態(tài)信號(hào)的排列熵值，選擇合適的尺度來(lái)構(gòu)建特征向量，選取一定數(shù)量的特征向量樣本并運(yùn)用支持向量機(jī)分類(lèi)器來(lái)對(duì)其進(jìn)行分類(lèi)，結(jié)果表明該方法對(duì)滾動(dòng)軸承的正常、內(nèi)圈故障、外圈故障、滾動(dòng)體故障這四種狀態(tài)具有很高的識(shí)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(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TH133.33;TH165.3

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