本文選題：滾動(dòng)軸承 + 故障診斷�。� 參考：《燕山大學(xué)》2012年碩士論文

【摘要】：隨著現(xiàn)代化工業(yè)生產(chǎn)的不斷發(fā)展，機(jī)械設(shè)備故障診斷技術(shù)近年來(lái)得到了廣泛的重視，滾動(dòng)軸承作為機(jī)械傳動(dòng)系統(tǒng)中的重要元件，其運(yùn)行的好壞直接影響機(jī)器的工作狀況。 針對(duì)滾動(dòng)軸承振動(dòng)信號(hào)噪聲，建立了一種數(shù)學(xué)形態(tài)學(xué)組合濾波器，通過(guò)組合形態(tài)濾波器對(duì)其振動(dòng)信號(hào)進(jìn)行降噪處理；針對(duì)振動(dòng)信號(hào)的非平穩(wěn)性、非線性等特征，提出一種多尺度形態(tài)學(xué)分析方法對(duì)故障信號(hào)進(jìn)行定性和定量分析，同時(shí)針對(duì)故障模式的模糊性問(wèn)題，提出采用模糊C均值(Fuzzy Center Means，簡(jiǎn)稱FCM)聚類算法的模糊故障識(shí)別方法，并將上述研究方法結(jié)合起來(lái)運(yùn)用到滾動(dòng)軸承的故障診斷中。 首先，闡述了滾動(dòng)軸承的故障主要形式和振動(dòng)機(jī)理，給出振動(dòng)信號(hào)常用降噪方法，如傳統(tǒng)的濾波方法、小波變化消噪技術(shù)和經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical modedecomposition，簡(jiǎn)稱EMD)降噪技術(shù)；同時(shí)闡述了傳統(tǒng)的振動(dòng)信號(hào)的分析方法，包括時(shí)域分析，頻域分析等。 其次，根據(jù)形態(tài)組合濾波器中結(jié)構(gòu)元素目前尚無(wú)一確定的選取準(zhǔn)則問(wèn)題，分析了形態(tài)組合濾波器中結(jié)構(gòu)元素的形狀、寬度和幅度對(duì)形態(tài)濾波效果的影響。 然后，分析了多尺度形態(tài)學(xué)在振動(dòng)信號(hào)中的應(yīng)用，通過(guò)分形維數(shù)和形態(tài)譜熵對(duì)故障信號(hào)進(jìn)行特征描述，將其作為描述故障的特征參數(shù)引入到模糊C均值聚類算法中作為聚類分析的特征向量，為機(jī)械故障識(shí)別作準(zhǔn)備。 最后，針對(duì)來(lái)自美國(guó)凱斯西儲(chǔ)大學(xué)的滾動(dòng)軸承故障數(shù)據(jù)及寶鋼1580SP軋機(jī)實(shí)測(cè)數(shù)據(jù)進(jìn)行實(shí)驗(yàn)研究及分析，，并給出結(jié)論。形態(tài)學(xué)濾波方法可以對(duì)滾動(dòng)軸承振動(dòng)信號(hào)達(dá)到很好的降噪效果；多尺度形態(tài)學(xué)方法可以對(duì)滾動(dòng)軸承故障進(jìn)行定性和定量描述，模糊C均值聚類可以取得良好的識(shí)別效果。
[Abstract]:With the development of modern industrial production, the fault diagnosis technology of mechanical equipment has been paid more and more attention in recent years. As an important component of mechanical transmission system, the running quality of rolling bearing directly affects the working condition of machinery. Aiming at the noise of rolling bearing vibration signal, a mathematical morphological combined filter is established, which is used to reduce the noise of the vibration signal, aiming at the non-stationary and nonlinear characteristics of the vibration signal. This paper presents a multi-scale morphological analysis method for qualitative and quantitative analysis of fault signals. Aiming at the fuzziness of fault mode, a fuzzy fault identification method using fuzzy C-means (FCM) clustering algorithm is proposed. The above research method is applied to the fault diagnosis of rolling bearing. Firstly, the main fault forms and vibration mechanism of rolling bearing are expounded, and the usual noise reduction methods of vibration signal are given, such as traditional filtering method, wavelet change noise elimination technique and empirical mode decomposition (EMD) noise reduction technology. At the same time, the traditional methods of vibration signal analysis, including time domain analysis, frequency domain analysis, etc. Secondly, the influence of shape, width and amplitude of structural elements on morphological filtering effect is analyzed according to the fact that there is no definite criterion for the selection of structural elements in morphological combinatorial filters. Then, the application of multi-scale morphology in vibration signal is analyzed. The fault signal is characterized by fractal dimension and morphological spectrum entropy. It is introduced into the fuzzy C-means clustering algorithm as the characteristic parameter to describe the fault, and it is used as the feature vector of the clustering analysis to prepare for the mechanical fault identification. Finally, the experimental research and analysis of rolling bearing fault data from case Western Reserve University and the measured data of Baosteel 1580SP mill are carried out, and the conclusions are given. Morphological filtering method can achieve good noise reduction effect on rolling bearing vibration signal, multi-scale morphological method can describe the rolling bearing fault qualitatively and quantitatively, and fuzzy C-means clustering can obtain good recognition effect.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3;TH133.33

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 左云波;王西彬;徐小力;;形態(tài)譜在發(fā)電機(jī)組故障趨勢(shì)分析中的應(yīng)用[J];北京理工大學(xué)學(xué)報(bào);2008年11期

2 趙協(xié)廣;戴炬;;基于平滑指數(shù)和小波的滾動(dòng)軸承故障診斷[J];軸承;2009年11期

3 郝衛(wèi)華;劉明光;;基于數(shù)學(xué)形態(tài)學(xué)的鐵路電力線路故障診斷方法[J];電力科學(xué)與工程;2008年01期

4 項(xiàng)明;吳小培;湯婷;孫林森;;基于模糊均值聚類的自適應(yīng)指紋圖像分割[J];電子測(cè)量技術(shù);2009年05期

5 簡(jiǎn)小剛;張艷偉;馮躍;;工程機(jī)械故障診斷技術(shù)的研究現(xiàn)狀與發(fā)展趨勢(shì)[J];中國(guó)工程機(jī)械學(xué)報(bào);2005年04期

6 蘇旭武,楊光友,周國(guó)柱;模糊數(shù)學(xué)在模式識(shí)別中應(yīng)用方法的比較[J];湖北工業(yè)大學(xué)學(xué)報(bào);2005年04期

7 姚桂艷,孫麗媛,程秀芳,薛全會(huì);機(jī)械故障診斷技術(shù)的研究現(xiàn)狀及發(fā)展趨勢(shì)[J];河北理工學(xué)院學(xué)報(bào);2005年03期

8 易挺;梁楚華;朱圓圓;;基于倒頻譜技術(shù)的滾動(dòng)軸承故障診斷[J];機(jī)床與液壓;2009年09期

9 趙永滿;梅衛(wèi)江;吳疆;王春林;;機(jī)械故障診斷技術(shù)發(fā)展及趨勢(shì)分析[J];機(jī)床與液壓;2009年10期

10 羅邦R

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