【摘要】：三相感應(yīng)電機(jī)結(jié)構(gòu)簡(jiǎn)單、運(yùn)行可靠且高效,被廣泛地應(yīng)用于生產(chǎn)生活的各個(gè)領(lǐng)域。滾動(dòng)軸承作為電機(jī)的重要組成部分很容易發(fā)生故障甚至?xí)斐蓢?yán)重的后果。因此,對(duì)軸承進(jìn)行定期的故障檢測(cè)及維護(hù),及早的發(fā)現(xiàn)故障并采取措施是至關(guān)重要的。本文針對(duì)軸承實(shí)際故障時(shí)損傷區(qū)域的大小、軸承動(dòng)力學(xué)結(jié)構(gòu)以及滾珠進(jìn)出坑時(shí)的承載力變化等情況,提出了軸承故障時(shí)的雙脈沖轉(zhuǎn)矩波動(dòng)模型,并基于該模型推導(dǎo)了定子電流中特征頻率的表達(dá)式。通過(guò)對(duì)雙脈沖模型下的定子電流信號(hào)分別進(jìn)行循環(huán)自相關(guān)函數(shù)和循環(huán)譜密度函數(shù)分析,相較于傳統(tǒng)的軸承故障特征提取與識(shí)別方法,發(fā)現(xiàn)循環(huán)平穩(wěn)理論在基頻及諧波的降噪、軸承故障特征頻率的識(shí)別與提取方面有明顯的優(yōu)越性。本文研究了軸承故障特征頻率的幅值與故障損傷寬度的關(guān)系,利用循環(huán)自相關(guān)函數(shù)分析了不同故障損傷寬度下,軸承故障特征頻率第一至第三邊頻的幅值變化規(guī)律。然后對(duì)定子電流信號(hào)進(jìn)行循環(huán)譜密度分析,可以看出其在循環(huán)頻率域與譜頻率域具有明顯的譜相關(guān)性,利用這種譜相關(guān)性可以從不同角度識(shí)別出故障特征,不僅僅能反映出循環(huán)自相關(guān)的信息,同時(shí)也會(huì)在譜頻率域中顯示出更多的故障特征信息,為軸承故障的識(shí)別提供了更多的判斷依據(jù)。在實(shí)驗(yàn)室環(huán)境下利用電機(jī)軸承故障實(shí)驗(yàn)平臺(tái)采集了電機(jī)不同故障寬度下的定子電流信號(hào),并對(duì)這些電流信號(hào)進(jìn)行循環(huán)自相關(guān)函數(shù)和循環(huán)譜密度函數(shù)分析。將實(shí)際分析結(jié)果與仿真結(jié)果進(jìn)行對(duì)比,驗(yàn)證了本文理論和方法的正確性,也顯示了循環(huán)平穩(wěn)理論的信號(hào)處理方法在識(shí)別和提取軸承故障特征頻率分量方面的優(yōu)越性。
[Abstract]:Three-phase induction motor with simple structure, reliable operation and high efficiency is widely used in various fields of production and life. As an important part of motor, rolling bearings are prone to failure and even serious consequences. Therefore, it is very important to detect and maintain the bearing regularly, to detect the fault as early as possible and to take measures. In this paper, a double pulse torque ripple model is proposed for bearing failure, such as the size of damage zone, the dynamic structure of bearing, and the change of bearing capacity when ball is entering or leaving the pit. Based on the model, the expression of characteristic frequency in stator current is derived. Through the analysis of the stator current signal under the double pulse model by the cyclic autocorrelation function and the cyclic spectral density function, compared with the traditional method of bearing fault feature extraction and identification, it is found that the cyclic stationary theory reduces the noise of the fundamental frequency and harmonics. Bearing fault feature frequency recognition and extraction has obvious advantages. In this paper, the relationship between the amplitude of bearing fault characteristic frequency and the fault damage width is studied. By using cyclic autocorrelation function, the variation of amplitude of bearing fault characteristic frequency from the first to the third edge frequency under different fault damage widths is analyzed. Then, by analyzing the cyclic spectral density of stator current signal, it can be seen that it has obvious spectral correlation between cyclic frequency domain and spectral frequency domain, and the fault characteristics can be identified from different angles by using this spectral correlation. It can not only reflect the cyclic autocorrelation information, but also show more fault characteristic information in the spectrum frequency domain, which provides more judgment basis for bearing fault identification. The stator current signals under different fault widths of motor are collected by using the motor bearing fault test platform in the laboratory, and the cyclic autocorrelation function and cyclic spectral density function are used to analyze these current signals. The comparison between the actual analysis results and the simulation results verifies the correctness of the theory and method in this paper, and also shows the superiority of the signal processing method of the cyclic stationary theory in identifying and extracting the frequency components of bearing fault characteristics.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM307

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本文編號(hào)：2320691