【摘要】：滾動軸承是旋轉(zhuǎn)機(jī)械中極易損壞的元件之一。據(jù)統(tǒng)計(jì),大約有30%的旋轉(zhuǎn)機(jī)械故障是由于軸承故障引起的。因此,對滾動軸承進(jìn)行狀態(tài)監(jiān)測與故障診斷具有很重要的意義。軸承故障沖擊信號是持續(xù)時(shí)間很短的脈沖狀波形,本文嘗試將沖擊信號從背景噪聲中截取出來,通過計(jì)算沖擊產(chǎn)生的頻率,來判斷沖擊的來源,從而檢測和診斷軸承工作狀態(tài)。本文主要研究內(nèi)容如下： (1)從滾動軸承振動信號的產(chǎn)生機(jī)理出發(fā),討論軸承運(yùn)行時(shí)產(chǎn)生的各種振動成分。并對軸承工作時(shí)各種影響因素進(jìn)行闡述。對軸承不同元件發(fā)生故障時(shí)產(chǎn)生的沖擊脈沖特征進(jìn)行研究。為滾動軸承故障診斷提供理論依據(jù)。 (2)對小波分析方法和傅立葉變換分析方法進(jìn)行對比分析。應(yīng)用小波分析實(shí)現(xiàn)對故障振動信號進(jìn)行瞬態(tài)檢測。在對沖擊脈沖進(jìn)行檢測基礎(chǔ)上對各個(gè)脈沖進(jìn)行分段截取。 (3)對分段信號進(jìn)行特征提取,包括時(shí)域特征、頻域特征、小波包能量特征。由于這些特征是從不同角度反映了沖擊脈沖的特性,但是,不同特征值反映脈沖性質(zhì)的能力也是不一樣的。由此需要引入主分量分析。通過介紹主分量分析算法原理、幾何意義,在特征提取后應(yīng)用主分量分析,結(jié)果使得少數(shù)幾個(gè)主分量就能很好地反映不同沖擊成分脈沖的性質(zhì),達(dá)到降維的目的。 (4)介紹聚類算法在軸承故障診斷中的應(yīng)用。詳細(xì)地介紹聚類算法中的距離定義、聚類準(zhǔn)則函數(shù)的定義以及聚類算法的分類,比較不同聚類算法的特點(diǎn)。討論模糊C均值聚類算法中模糊指數(shù)和聚類數(shù)兩參數(shù)選取對聚類結(jié)果的影響。 最后,通過兩個(gè)模擬軸承故障實(shí)驗(yàn)來驗(yàn)證本文研究方法的有效性。一個(gè)是對外圈單一故障類型進(jìn)行診斷,另一個(gè)是對外圈故障和滾動體故障的混合故障類型進(jìn)行診斷。本文研究表明,基于分段聚類的軸承故障診斷方法是可行的,算法運(yùn)算簡單、可靠,對軸承進(jìn)行精確的診斷具有很重要的意義。
[Abstract]:Rolling bearing is one of the most vulnerable components in rotating machinery. According to statistics, about 30% of rotating machinery failures are caused by bearing failures. Therefore, the condition monitoring and fault diagnosis of rolling bearings is of great significance. Bearing fault shock signal is a pulse waveform with short duration. This paper attempts to intercept the impact signal from the background noise and determine the source of the impact by calculating the frequency of the impact, so as to detect and diagnose the working state of the bearing. The main contents of this paper are as follows: (1) based on the mechanism of vibration signal of rolling bearing, the vibration components of bearing are discussed. And the bearing work of various factors are described. The impact pulse characteristics of different bearing components are studied. It provides theoretical basis for rolling bearing fault diagnosis. (2) the wavelet analysis method and Fourier transform analysis method are compared and analyzed. The transient detection of fault vibration signal is realized by wavelet analysis. On the basis of impulse detection, each pulse is segmented. (3) feature extraction of segmented signal, including time domain feature, frequency domain feature, wavelet packet energy feature. Because these characteristics reflect the characteristics of impulse pulses from different angles, however, the ability of different eigenvalues to reflect the properties of pulses is also different. It is necessary to introduce principal component analysis (PCA). By introducing the principle of principal component analysis (PCA) algorithm and geometric meaning, applying PCA after feature extraction, the results show that a few principal components can well reflect the properties of different impulse components. The purpose of dimension reduction is achieved. (4) the application of clustering algorithm in bearing fault diagnosis is introduced. The definition of distance, the definition of clustering criterion function and the classification of clustering algorithm are introduced in detail, and the characteristics of different clustering algorithms are compared. The influence of two parameters of fuzzy index and clustering number on the clustering results in fuzzy C-means clustering algorithm is discussed. Finally, the effectiveness of this method is verified by two simulated bearing failure experiments. One is to diagnose the single fault type of outer ring, the other is to diagnose the mixed fault type of outer ring fault and rolling body fault. This paper shows that the method of bearing fault diagnosis based on piecewise clustering is feasible, the algorithm is simple and reliable, and the accurate diagnosis of bearing is of great significance.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH133.31;TH165.3

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