【摘要】：滾動(dòng)軸承是各種旋轉(zhuǎn)機(jī)械中應(yīng)用較廣泛且易損壞的機(jī)械零部件之一，它的運(yùn)行狀態(tài)往往直接影響著整臺(tái)機(jī)器的性能。對(duì)滾動(dòng)軸承進(jìn)行可靠的故障診斷能及時(shí)地發(fā)現(xiàn)故障，能有效地避免機(jī)器的損壞和人員傷亡等重大事故。特別在軋鋼機(jī)械中，滾動(dòng)軸承更是特別重要的零件，故研究滾動(dòng)軸承故障診斷具有現(xiàn)實(shí)的意義。 利用電火花破壞滾動(dòng)軸承用以模擬軸承故障，采用模擬故障代替實(shí)際故障進(jìn)行試驗(yàn)研究。搭建滾動(dòng)軸承故障實(shí)驗(yàn)平臺(tái)并在平臺(tái)上采集振動(dòng)信號(hào)用于試驗(yàn)研究。 本文首先分析了滾動(dòng)軸承的故障機(jī)理和振動(dòng)特性，并總結(jié)了滾動(dòng)軸承局部損傷類故障振動(dòng)信號(hào)的周期性沖擊和幅值調(diào)制的特性；然后為有效地提取滾動(dòng)軸承故障特征，提出了一種基于經(jīng)驗(yàn)?zāi)B(tài)分解法和獨(dú)立分量分析法相結(jié)合的故障特征提取的方法，并應(yīng)用到實(shí)際的滾動(dòng)軸承實(shí)驗(yàn)數(shù)據(jù)處理和分析中，，表明能準(zhǔn)確地提取滾動(dòng)軸承的故障特征；其次，對(duì)支持向量機(jī)的理論做了系統(tǒng)研究，提出用基于交叉驗(yàn)證的遺傳算法對(duì)支持向量機(jī)中懲罰參數(shù)C和高斯核參數(shù)γ進(jìn)行尋優(yōu)，確保了分類模型的最優(yōu)性；最后，提出了利用分離矩陣的奇異值作為滾動(dòng)軸承故障特征向量的方法，通過(guò)實(shí)際滾動(dòng)軸承振動(dòng)實(shí)驗(yàn)并采集數(shù)據(jù)，驗(yàn)證了該方法能較好的鑒別故障類型。 采用MATLAB和LabVIEW相結(jié)合的方法完成了對(duì)滾動(dòng)軸承智能故障診斷系統(tǒng)平臺(tái)的開(kāi)發(fā)。該平臺(tái)界面友好，易于操作；通過(guò)在滾動(dòng)軸承實(shí)驗(yàn)臺(tái)上模擬滾動(dòng)軸承的典型故障的振動(dòng)試驗(yàn)，結(jié)果證明該診斷系統(tǒng)性能良好。
[Abstract]:Rolling bearing is one of the mechanical parts which are widely used and easy to be damaged in various rotating machinery. Its running state often directly affects the performance of the whole machine. Reliable fault diagnosis of rolling bearings can find faults in time, and can effectively avoid serious accidents such as machine damage and casualties. Especially in rolling machinery, rolling bearing is a very important part, so it is of practical significance to study the fault diagnosis of rolling bearing. Using the electric spark to destroy the rolling bearing is used to simulate the bearing fault, and the simulation fault is used to replace the actual fault to carry on the experiment research. A rolling bearing fault test platform is set up and vibration signals are collected on the platform for experimental research. In this paper, the fault mechanism and vibration characteristics of rolling bearings are analyzed firstly, and the periodic shock and amplitude modulation characteristics of local damage vibration signals of rolling bearings are summarized. Then, in order to extract the fault feature of rolling bearing effectively, a fault feature extraction method based on the combination of empirical mode decomposition method and independent component analysis method is proposed, which is applied to the practical data processing and analysis of rolling bearing experiment. It is shown that the fault characteristics of rolling bearing can be extracted accurately. Secondly, the theory of support vector machine is studied systematically, and the genetic algorithm based on cross-validation is proposed to optimize the penalty parameter C and Gao Si kernel parameter 緯 in support vector machine, which ensures the optimality of classification model. Finally, the singular value of the separation matrix is used as the fault characteristic vector of rolling bearing. Through the vibration experiment of the rolling bearing and the collection of data, the method is proved to be effective in identifying the fault types. The intelligent fault diagnosis system platform of rolling bearing is developed by means of MATLAB and LabVIEW. The platform has a friendly interface and is easy to operate, and the performance of the diagnosis system is proved to be good by simulating the vibration tests of typical faults of rolling bearings on the rolling bearing test stand.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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