本文選題：故障診斷　切入點(diǎn)：滾動(dòng)軸承　出處：《沈陽(yáng)大學(xué)》2012年碩士論文

【摘要】：滾動(dòng)軸承是現(xiàn)代工業(yè)生產(chǎn)中最為常用的一種部件,尤其是在旋轉(zhuǎn)機(jī)械當(dāng)中使用更為廣泛。滾動(dòng)軸承高發(fā)的故障率,給生產(chǎn)過(guò)程帶來(lái)了巨大的影響。它威脅到生產(chǎn)安全,同時(shí)也會(huì)對(duì)經(jīng)濟(jì)利益造成損失。因此,對(duì)滾動(dòng)軸承故障診斷的研究十分具有意義。 本文主要以功率譜分析和數(shù)據(jù)驅(qū)動(dòng)方法為理論基礎(chǔ),提出了利用主成分分析和費(fèi)舍爾判別分析的方法來(lái)研究功率譜分析的滾動(dòng)軸承的各類(lèi)振動(dòng)信號(hào)。主要研究工作如下: 1.本文對(duì)滾動(dòng)軸承的故障機(jī)理及振動(dòng)特征進(jìn)行了詳細(xì)分析,對(duì)幾種主要的軸承故障形式以及主要的故障診斷方法進(jìn)行了討論分析。采用頻域分析方法對(duì)軸承振動(dòng)信號(hào)進(jìn)行特征提取,并進(jìn)一步利用主成分分析法對(duì)提取的特征進(jìn)行篩選,降低信號(hào)特征向量的維數(shù),以提高故障診斷的準(zhǔn)確性和實(shí)時(shí)性。 2.滾動(dòng)軸承故障發(fā)生時(shí),其振動(dòng)信號(hào)在某些頻段內(nèi)的信號(hào)能量分布會(huì)出現(xiàn)變化。分別根據(jù)正常數(shù)據(jù)和不同故障數(shù)據(jù)構(gòu)建故障診斷模型,然后用T 2統(tǒng)計(jì)和Q統(tǒng)計(jì)的方法來(lái)檢測(cè)不同的故障。通過(guò)對(duì)比識(shí)別準(zhǔn)確率,對(duì)不同數(shù)據(jù)構(gòu)建的模型的優(yōu)劣進(jìn)行了對(duì)比分析。 3.為了解決對(duì)不同軸承故障進(jìn)行分類(lèi)的問(wèn)題,提出利用費(fèi)舍爾判別分析在分類(lèi)中最大化類(lèi)間、最小化類(lèi)內(nèi)離散度的特點(diǎn),可以對(duì)不同位置的故障以及相同位置不同大小的故障進(jìn)行分類(lèi)。在仿真試驗(yàn)中分類(lèi)準(zhǔn)確率比較理想。 仿真實(shí)驗(yàn)結(jié)果表明,本文所提出的方法能較為準(zhǔn)確地分辨出軸承的正常和故障狀態(tài),并對(duì)故障的位置及大小進(jìn)行識(shí)別,可以較好地解決滾動(dòng)軸承故障診斷的問(wèn)題。
[Abstract]:Rolling bearing is one of the most commonly used parts in modern industrial production, especially widely used in rotating machinery.The high failure rate of rolling bearing has brought great influence to the production process.It is a threat to production safety, but also to the loss of economic benefits.Therefore, the study of rolling bearing fault diagnosis is of great significance.Based on the theory of power spectrum analysis and data-driven method, this paper presents a method of principal component analysis and Fisher discriminant analysis to study the vibration signals of rolling bearings based on power spectrum analysis.The main work of the study is as follows:1.In this paper, the fault mechanism and vibration characteristics of rolling bearing are analyzed in detail, and several main bearing fault forms and main fault diagnosis methods are discussed and analyzed.In order to improve the accuracy and real time of fault diagnosis, the feature extraction of bearing vibration signal is carried out by frequency domain analysis, and the feature extracted is screened by principal component analysis (PCA) to reduce the dimension of signal feature vector.2.When the rolling bearing fault occurs, the signal energy distribution of the vibration signal in some frequency bands will change.Fault diagnosis models are constructed according to normal data and different fault data, and different faults are detected by T 2 statistics and Q statistics.By comparing the accuracy of recognition, the merits and demerits of the models constructed by different data are compared and analyzed.3.In order to solve the problem of classifying different bearing faults, Fisher discriminant analysis is proposed to maximize inter-class and minimize intra-class dispersion.Faults in different locations and different sizes in the same position can be classified.The classification accuracy is ideal in the simulation experiment.The simulation results show that the method proposed in this paper can accurately distinguish the normal and fault state of the bearing, and identify the position and size of the fault, which can solve the problem of fault diagnosis of rolling bearing.
【學(xué)位授予單位】：沈陽(yáng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH165.3

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