發(fā)布時(shí)間：2018-09-18 21:28

【摘要】：滾動(dòng)軸承是旋轉(zhuǎn)機(jī)械中的關(guān)鍵部件，也是容易出現(xiàn)故障的部件，對(duì)其進(jìn)行故障診斷是國(guó)內(nèi)外工程技術(shù)領(lǐng)域一直非常關(guān)注的課題。據(jù)大量的研究事實(shí)證明，，目前對(duì)滾動(dòng)軸承的狀態(tài)進(jìn)行監(jiān)測(cè)與診斷，最實(shí)用的方法是振動(dòng)信號(hào)分析法。測(cè)取的振動(dòng)信號(hào)是非平穩(wěn)、非線性的，經(jīng)驗(yàn)?zāi)B(tài)分解方法是自適應(yīng)的分解方法，尤其適用于非線性、非平穩(wěn)信號(hào)的分解。 本文首先介紹了滾動(dòng)軸承的故障模式和發(fā)生故障的機(jī)理，計(jì)算了實(shí)驗(yàn)所用軸承的特征頻率和固有頻率，通過(guò)對(duì)采集的振動(dòng)信號(hào)進(jìn)行時(shí)頻分析得到了故障頻率，通過(guò)對(duì)比理論計(jì)算和軟件計(jì)算得到的結(jié)果，對(duì)軸承的故障模式做了初步診斷，得到了各故障模式的頻譜特征。 本文主要利用小波精確的頻帶換分優(yōu)勢(shì)對(duì)采集的振動(dòng)信號(hào)進(jìn)行去噪，嘗試使用平移不變量方法對(duì)采集的信號(hào)去噪，取得了不錯(cuò)的效果。然后對(duì)去噪后的信號(hào)進(jìn)行經(jīng)驗(yàn)?zāi)B(tài)分解法分解獲得了本征模函數(shù)分量，由于信號(hào)的特征主要集中在前幾個(gè)分量，計(jì)算了前7個(gè)IMF的能量組成特征向量，結(jié)合神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)兩種方法對(duì)滾動(dòng)軸承的工作狀況做出診斷，對(duì)兩種方法的識(shí)別效果做了比較。
[Abstract]:Rolling bearing is one of the key parts in rotating machinery, and it is also prone to malfunction. Fault diagnosis of rolling bearing is a subject of great concern in the field of engineering and technology at home and abroad. According to a large number of research facts, the most practical method to monitor and diagnose the status of rolling bearings is vibration signal analysis. The measured vibration signal is nonstationary and nonlinear. The empirical mode decomposition method is an adaptive decomposition method, which is especially suitable for the decomposition of nonlinear and non-stationary signals. In this paper, the fault mode and fault mechanism of rolling bearing are introduced, the characteristic frequency and natural frequency of bearing used in experiment are calculated, and the fault frequency is obtained by time-frequency analysis of vibration signal collected. By comparing the results of theoretical calculation and software calculation, the fault modes of bearings are preliminarily diagnosed, and the spectrum characteristics of each fault mode are obtained. In this paper, we mainly use the advantage of frequency band switching of wavelet to Denoise the collected vibration signal, and try to use the method of translation invariant to de-noise the collected signal, and get good results. Then the eigenmode function components of the denoised signal are obtained by empirical mode decomposition. Because the characteristics of the signal are mainly concentrated in the first few components, the energy component Eigenvectors of the first seven IMF are calculated. Neural network and support vector machine are used to diagnose the working condition of rolling bearing, and the recognition effect of the two methods is compared.
【學(xué)位授予單位】：西安電子科技大學(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

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本文編號(hào)：2249143