基于循環(huán)平穩(wěn)的電機軸承故障特征分析
發(fā)布時間：2018-11-09 14:33
【摘要】：三相感應電機結構簡單、運行可靠且高效,被廣泛地應用于生產(chǎn)生活的各個領域。滾動軸承作為電機的重要組成部分很容易發(fā)生故障甚至會造成嚴重的后果。因此,對軸承進行定期的故障檢測及維護,及早的發(fā)現(xiàn)故障并采取措施是至關重要的。本文針對軸承實際故障時損傷區(qū)域的大小、軸承動力學結構以及滾珠進出坑時的承載力變化等情況,提出了軸承故障時的雙脈沖轉(zhuǎn)矩波動模型,并基于該模型推導了定子電流中特征頻率的表達式。通過對雙脈沖模型下的定子電流信號分別進行循環(huán)自相關函數(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系,利用循環(huán)自相關函數(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。然后對定子電流信號進行循環(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性,利用這種譜相關性可以從不同角度識別出故障特征,不僅僅能反映出循環(huán)自相關的信息,同時也會在譜頻率域中顯示出更多的故障特征信息,為軸承故障的識別提供了更多的判斷依據(jù)。在實驗室環(huán)境下利用電機軸承故障實驗平臺采集了電機不同故障寬度下的定子電流信號,并對這些電流信號進行循環(huán)自相關函數(shù)和循環(huán)譜密度函數(shù)分析。將實際分析結果與仿真結果進行對比,驗證了本文理論和方法的正確性,也顯示了循環(huán)平穩(wěn)理論的信號處理方法在識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.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM307
本文編號：2320691
[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.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM307
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，本文編號：2320691
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