發(fā)布時間：2018-04-18 03:11

  本文選題：狀態(tài)監(jiān)測 + 故障診斷　； 參考：《中國科學技術(shù)大學》2011年碩士論文

【摘要】：在軸承的狀態(tài)監(jiān)測與故障診斷研究中,最主要的就是要找到最能體現(xiàn)軸承故障本質(zhì)的特征量,故障特征的選則和提取是軸承故障研究中的關(guān)鍵之一。本論文選取能體現(xiàn)信號復(fù)雜程度的分形維數(shù)作為特征量,并分別從信號的時域和頻域來計算信號的分形維數(shù)。 第一章首先闡述了軸承故障診斷技術(shù)的選題背景與意義,以及軸承故障診斷系統(tǒng)中主要的研究內(nèi)容;然后簡要地介紹和比較了現(xiàn)有的檢測方案與所采用的技術(shù)手段;然后分析了振動信號分析的處理方法。包括傳統(tǒng)振動信號處理方法:時域統(tǒng)計量分析、Fourier分析、倒頻譜分析、包絡(luò)分析等方法;現(xiàn)代信號處理方法:短時Fourier變換、小波變換、雙線性時頻分析、循環(huán)平穩(wěn)分析、分形分析,并分析了各種方法的優(yōu)缺點與適應(yīng)范圍。 第二章首先介紹了軸承的主要失效類型包括:磨損失效、疲勞失效、斷裂失效、塑性變形失效等。接著重點計算了軸承在不受軸向力和受軸向力時的外圈故障特征頻率、內(nèi)圈故障特征頻率和滾子故障特征頻率。最后研究了軸承發(fā)生故障時,軸承振動信號的時域特性。 第三章選用了一種與盒維數(shù)等價的網(wǎng)格維數(shù)來進行軸承振動信號的特征提取,通過改變采樣點數(shù),計算出選取不同采樣點數(shù)的振動信號的網(wǎng)格維數(shù),由不同的采樣點數(shù)計算出的網(wǎng)格維數(shù)組成軸承狀態(tài)的特征向量,并進一步建立軸承狀態(tài)模式空間,根據(jù)未知軸承狀態(tài)的特征向量與模式空間中的特征向量之間的距離大小來判斷未知狀態(tài)與已知狀態(tài)的接近程度。 第四章主要研究了基于小波變換的分形方法在軸承狀態(tài)分類中的應(yīng)用。首先對信號進行二進離散小波變換,再通過二進離散小波反變換得到不同尺度下的細節(jié)信號,根據(jù)原始信號的功率譜特點與1/ f過程的關(guān)系,選取1/ f過程的分形維數(shù)作為特征量,通過計算這些尺度下各細節(jié)信號的方差,再取對數(shù),再進行直線擬合,得到斜率,根據(jù)斜率求出分形維數(shù),通過對分形維數(shù)的分類再對軸承狀態(tài)進行分類。根據(jù)最后的計算結(jié)果,此種方法可以有效的區(qū)分軸承的正常、外圈故障、滾珠故障這三種狀態(tài)。 第五章總結(jié)本論文的成果與不足并提出了研究展望。
[Abstract]:In the research of bearing condition monitoring and fault diagnosis, the most important thing is to find the characteristic quantity which can best reflect the nature of bearing fault. The selection and extraction of fault feature is one of the key points in bearing fault research.In this paper, the fractal dimension, which can reflect the complexity of the signal, is selected as the characteristic, and the fractal dimension of the signal is calculated from the time domain and the frequency domain respectively.The first chapter introduces the background and significance of bearing fault diagnosis technology, and the main research contents of bearing fault diagnosis system, and then briefly introduces and compares the existing detection schemes and technical means.Then the processing method of vibration signal analysis is analyzed.Traditional vibration signal processing methods include time-domain statistical analysis Fourier analysis, cepstrum analysis, envelope analysis, and modern signal processing methods: short time Fourier transform, wavelet transform, bilinear time-frequency analysis, cyclic stationary analysis, etc.Fractal analysis and analysis of the advantages and disadvantages of various methods and the scope of adaptation.The second chapter introduces the main failure types of bearing, such as wear failure, fatigue failure, fracture failure, plastic deformation failure and so on.Then, the fault characteristic frequency of outer ring, inner ring and roller are calculated.Finally, the time domain characteristic of bearing vibration signal is studied.In chapter 3, a grid dimension equivalent to the box dimension is selected to extract the feature of the bearing vibration signal. By changing the sampling points, the grid dimension of the vibration signal with different sampling points is calculated.The characteristic vector of bearing state is made up of the grid dimension calculated by different sampling points, and the bearing state mode space is further established.According to the distance between the eigenvector of the unknown bearing state and the eigenvector in the pattern space, the degree of proximity between the unknown state and the known state is determined.In chapter 4, the application of fractal method based on wavelet transform in bearing state classification is studied.Firstly, the signal is transformed by dyadic discrete wavelet transform, and then the detail signals of different scales are obtained by using the dyadic discrete wavelet inverse transform. According to the power spectrum characteristics of the original signal and the relationship between the 1 / f process and the power spectrum of the original signal,The fractal dimension of 1 / f process is selected as the characteristic quantity. By calculating the variance of each detail signal under these scales, then taking the logarithm, the slope is obtained by straight line fitting, and the fractal dimension is calculated according to the slope.The state of bearing is classified by the classification of fractal dimension.According to the final calculation results, this method can effectively distinguish the normal bearing, the outer ring fault and the ball fault.The fifth chapter summarizes the achievements and shortcomings of this paper and puts forward the research prospect.
【學位授予單位】：中國科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關(guān)期刊論文 前1條

1 王冰;李洪儒;許葆華;;基于數(shù)學形態(tài)學分段分形維數(shù)的電機滾動軸承故障模式識別[J];振動與沖擊;2013年19期

相關(guān)碩士學位論文 前1條

1 朱美臣;電機軸承故障診斷[D];沈陽理工大學;2013年

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本文編號：1766525