本文選題：滾動軸承 + 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，機械設(shè)備故障診斷技術(shù)近年來得到了廣泛的重視，滾動軸承作為機械傳動系統(tǒng)中的重要元件，其運行的好壞直接影響機器的工作狀況。 針對滾動軸承振動信號噪聲，建立了一種數(shù)學(xué)形態(tài)學(xué)組合濾波器，通過組合形態(tài)濾波器對其振動信號進行降噪處理；針對振動信號的非平穩(wěn)性、非線性等特征，提出一種多尺度形態(tài)學(xué)分析方法對故障信號進行定性和定量分析，同時針對故障模式的模糊性問題，提出采用模糊C均值(Fuzzy Center Means，簡稱FCM)聚類算法的模糊故障識別方法，并將上述研究方法結(jié)合起來運用到滾動軸承的故障診斷中。 首先，闡述了滾動軸承的故障主要形式和振動機理，給出振動信號常用降噪方法，如傳統(tǒng)的濾波方法、小波變化消噪技術(shù)和經(jīng)驗?zāi)B(tài)分解(Empirical modedecomposition，簡稱EMD)降噪技術(shù)；同時闡述了傳統(tǒng)的振動信號的分析方法，包括時域分析，頻域分析等。 其次，根據(jù)形態(tài)組合濾波器中結(jié)構(gòu)元素目前尚無一確定的選取準則問題，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)在振動信號中的應(yīng)用，通過分形維數(shù)和形態(tài)譜熵對故障信號進行特征描述，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，為機械故障識別作準備。 最后，針對來自美國凱斯西儲大學(xué)的滾動軸承故障數(shù)據(jù)及寶鋼1580SP軋機實測數(shù)據(jù)進行實驗研究及分析，，并給出結(jié)論。形態(tài)學(xué)濾波方法可以對滾動軸承振動信號達到很好的降噪效果；多尺度形態(tài)學(xué)方法可以對滾動軸承故障進行定性和定量描述，模糊C均值聚類可以取得良好的識別效果。
[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)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TH133.33

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10 羅邦R

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