本文選題：滾動軸承 + 振動信號��； 參考：《湖南科技大學(xué)》2012年碩士論文

【摘要】：在復(fù)雜多變的工業(yè)現(xiàn)場，滾動軸承具有高事故率、故障高危險性。為有效保障生產(chǎn)效率和人員安全，對滾動軸承出現(xiàn)的故障進(jìn)行高效、快捷、準(zhǔn)確的識別和診斷就顯得非常重要。然而滾動軸承因運(yùn)行環(huán)境復(fù)雜而使得故障診斷中采集的振動信號被噪聲湮沒，給故障特征提取帶來極大的不便，尤其在故障特征微弱或是故障發(fā)生早期。有效實現(xiàn)滾動軸承的故障振動信號消噪，并且研究分析適合于滾動軸承故障振動信號的消噪方法，，對有效提高滾動軸承設(shè)備狀態(tài)監(jiān)測和故障診斷的精度和效率具有重要的意義。 首先，對滾動軸承的正常軸承、內(nèi)圈故障、外圈故障和滾珠故障的振動信號和噪聲情況進(jìn)行對比，分析軸承振動噪聲的分布、能量特性。 其次，提出了基于自相關(guān)和閾值的經(jīng)驗?zāi)B(tài)分解消噪方法。在分析經(jīng)驗?zāi)B(tài)分解方法的消噪性能后，采用噪聲自相關(guān)特性識別噪聲模態(tài)和并對其閾值處理，以實現(xiàn)信號重構(gòu)消噪的方法，可以有效識別經(jīng)驗?zāi)B(tài)分解后各模態(tài)分量中噪聲占主導(dǎo)的模態(tài)分量，和盡可能減少信號重構(gòu)時有用成分損失。并用仿真信號驗證本方法的消噪效果。 再次，提出了基于自相關(guān)集成經(jīng)驗?zāi)B(tài)分解消噪和基于自適應(yīng)的集成經(jīng)驗?zāi)B(tài)分解消噪方法。采用克服了模態(tài)混疊問題的集成經(jīng)驗?zāi)B(tài)分解方法，結(jié)合自相關(guān)分選和閾值處理，實現(xiàn)集成經(jīng)驗?zāi)B(tài)分解的消噪；在分析信號模態(tài)中噪聲能量的特點，自適應(yīng)生成閾值實現(xiàn)消噪處理，從而提出自適應(yīng)的集成經(jīng)驗?zāi)B(tài)分解消噪。采用仿真信號驗證了集成經(jīng)驗?zāi)B(tài)分解消噪的性能。 最后，對滾動軸承內(nèi)圈故障振動信號和外圈故障振動信號進(jìn)行消噪分析，并與常用消噪方法作對比，本文所提新方法能有效識別軸承故障特征頻率和工頻，具有比常用方法更好的消噪效果。 本文通過對滾動軸承故障振動信號和噪聲分析，使用改進(jìn)的經(jīng)驗?zāi)B(tài)分解消噪方法對滾動軸承故障振動信號進(jìn)行有效消噪，為滾動軸承狀態(tài)監(jiān)測和故障診斷提供有效的信號預(yù)處理方法。
[Abstract]:In complex and changeable industrial field, rolling bearing has high accident rate and high fault risk. In order to ensure production efficiency and personnel safety, it is very important to identify and diagnose the faults of rolling bearings efficiently, quickly and accurately. However, because of the complex running environment, the vibration signal collected in fault diagnosis is obliterated by noise, which brings great inconvenience to fault feature extraction, especially in the early stage of fault feature weak or fault. The de-noising of the fault vibration signal of rolling bearing is realized effectively, and the method of de-noising suitable for the fault vibration signal of rolling bearing is studied and analyzed. It is of great significance to improve the accuracy and efficiency of condition monitoring and fault diagnosis of rolling bearing equipment. Firstly, the vibration signal and noise of normal bearing, inner ring fault, outer ring fault and ball fault of rolling bearing are compared. The distribution and energy characteristics of bearing vibration noise are analyzed. Secondly, an empirical mode decomposition de-noising method based on autocorrelation and threshold is proposed. After analyzing the denoising performance of the empirical mode decomposition method, the noise mode is identified by the noise autocorrelation characteristic and the threshold value is processed to realize the de-noising method of signal reconstruction. It can effectively identify the noise-dominated modal components after empirical mode decomposition and minimize the loss of useful components in signal reconstruction. Simulation signals are used to verify the denoising effect of the proposed method. Thirdly, an integrated empirical mode decomposition de-noising method based on autocorrelation and an adaptive integrated empirical mode decomposition de-noising method are proposed. The method of integrated empirical mode decomposition, which overcomes the problem of modal aliasing, is adopted to realize the de-noising of integrated empirical mode decomposition by combining autocorrelation sorting and threshold processing, and the characteristics of noise energy in signal modes are analyzed. Adaptive threshold is generated to realize denoising, and an adaptive integrated empirical mode decomposition (EMD) is proposed. The performance of integrated empirical mode decomposition (EMD) de-noising is verified by simulation signal. Finally, the de-noising signal of inner ring fault vibration signal and outer ring fault vibration signal of rolling bearing is analyzed, and compared with the usual de-noising method. The new method proposed in this paper can effectively identify the bearing fault characteristic frequency and power frequency, and has better denoising effect than the usual method. The vibration signal and noise of rolling bearing fault are analyzed in this paper. The improved empirical mode decomposition (EMD) denoising method is used to effectively de-noise the rolling bearing fault vibration signal, which provides an effective signal preprocessing method for the rolling bearing condition monitoring and fault diagnosis.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TN911.7

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本文編號：1994108