本文選題：小波閾值降噪 + LMD�。� 參考：《內(nèi)蒙古大學》2015年碩士論文

【摘要】：旋轉(zhuǎn)機械設備在現(xiàn)代化工業(yè)生產(chǎn)中發(fā)揮著不可小覷的作用,其正常運轉(zhuǎn)是安全生產(chǎn)的重要保障。運用新興的信號處理與故障特征提取方法對旋轉(zhuǎn)機械進行實時檢測與故障診斷,能在一定程度上保障機械設備系統(tǒng)安全高效的運行。滾動軸承是旋轉(zhuǎn)機械設備的重要組成部分與典型代表,本文針對滾動軸承故障振動數(shù)據(jù)進行信號處理、故障特征提取與初步診斷的研究。實際采集到的振動信號中不僅包含軸承振動信號,而且還混入大量的噪聲信號,這將嚴重影響信號分解以及后續(xù)的故障特征提取。因此,采用一定的方法進行信號降噪預處理則顯得非常必要。本文以信噪比、最小均方根誤差作為降噪性能的判別依據(jù),通過仿真實驗確定了最適合本文振動信號的小波函數(shù)、分解層數(shù)、閾值選擇規(guī)則以及閾值函數(shù)并將其用于振動信號的降噪處理。鑒于LMD算法的自適應信號分解等特點,將其用于降噪振動信號分解得到乘積函數(shù)分量。通過分析故障振動信號的特點,提出了采用能量熵、奇異值熵、峭度以及Lemple-Ziv復雜度算法,從乘積函數(shù)分量中提取出故障特征并用于初步故障診斷。實驗結果表明,軸承在正常運行狀態(tài)下的能量熵和奇異值熵均大于內(nèi)圈、滾動體以及外圈三種故障狀態(tài)下的對應值,而峭度以及Lemple-Ziv復雜度指標在正常狀態(tài)下要小于故障狀態(tài)。綜上所述,本文采用的小波閾值降噪方法能很好地實現(xiàn)信號的降噪預處理,運用LMD方法進行振動信號分解后提取的能量熵、奇異值熵、峭度以及Lemple-Ziv復雜度故障特征均能有效地實現(xiàn)滾動軸承的初步故障診斷。
[Abstract]:Rotating machinery plays an important role in modern industrial production, and its normal operation is an important guarantee of safe production. Using the new signal processing and fault feature extraction method to detect and diagnose the rotating machinery in real time can ensure the safe and efficient operation of the mechanical equipment system to some extent. Rolling bearing is an important part and typical representative of rotating machinery. In this paper, the signal processing, fault feature extraction and preliminary diagnosis of rolling bearing fault vibration data are studied. The actual vibration signals not only contain bearing vibration signals, but also mix with a large number of noise signals, which will seriously affect the signal decomposition and subsequent fault feature extraction. Therefore, it is very necessary to adopt certain method for signal denoising preprocessing. In this paper, the signal-to-noise ratio (SNR) and the minimum root mean square error (MMSE) are taken as the basis for judging the performance of noise reduction. The threshold selection rule and threshold function are applied to the noise reduction of vibration signal. In view of the characteristics of adaptive signal decomposition of LMD algorithm, it is used to decompose noise and vibration signal to get the product function component. By analyzing the characteristics of the fault vibration signal, an algorithm using energy entropy, singular value entropy, kurtosis and Lemple-Ziv complexity algorithm is proposed to extract the fault features from the product function component and apply them to the preliminary fault diagnosis. The experimental results show that the energy entropy and singular value entropy of the bearing under normal operation are larger than those of the inner ring, the rolling body and the outer ring, but the kurtosis and the Lemple-Ziv complexity index are smaller than the fault state in the normal state. To sum up, the wavelet threshold de-noising method used in this paper can achieve the signal de-noising preprocessing well, and the energy entropy, singular value entropy extracted from vibration signal decomposition by using LMD method, Kurtosis and Lemple-Ziv complexity fault features can effectively realize the initial fault diagnosis of rolling bearings.
【學位授予單位】：內(nèi)蒙古大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TH165.3

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