本文關鍵詞：滾動軸承振動信號降噪方法研究　出處：《華北電力大學(北京)》2016年碩士論文　論文類型：學位論文

  更多相關文章： 小波變換 振動 軸承 降噪處理

【摘要】：大型旋轉機械設備中的滾動軸承是旋轉機械中關鍵且容易損壞的部件,旦出現(xiàn)故障,將會導致機械設備運行失穩(wěn)等一系列嚴重后果,而故障部位提取的振動信號往往表現(xiàn)出波動特征。滾動軸承振動信號包含了大量的軸承運行狀態(tài)信息,是對軸承開展故障分析的主要依據(jù)之一。因此,論文針對大型旋轉機械的滾動軸承振動的仿真信號和實測信號分別開展了信號降噪方法研究,將對進一步處理振動監(jiān)測信號、預防安全事故發(fā)生都具有重要的理論指導意義和工程實際應用價值。論文研究的主要目的是利用大型旋轉機械設備滾動軸承振動信號進行降噪處理,提取出所需要的振動信號。針對振動信號非平穩(wěn)的特點,信號降噪研究了基于改進的小波半軟閡值降噪方法。特征提取是實現(xiàn)機械故障診斷的關鍵步驟,對與滾動軸承振動故障診斷至為關鍵。論文首先介紹了小波分析的基本原理,給出了小波變換的基本定義和基本性質,介紹了常見小波變換的基本算法。然后介紹了傳統(tǒng)的基于小波變換的硬閾值降噪方法和基于小波變換的軟閾值降噪方法,在分析它們的優(yōu)點和缺點基、礎上,針對它們存在的缺點和不足,提出了改進的基于小波變換的半軟閾值降噪方法。最后給出了評價各降噪方法的性能評價準則。針對滾動軸承振動降噪問題采用改進的基于小波變換的半軟閾值降噪方法分別進行了仿真信號測試和實際信號測試分析,最后通過性能評價指標、頻譜分析對比了這三種方法的性能優(yōu)劣,結果表明所提出的基于小波變換的半軟閾值降噪方法比傳統(tǒng)的基于小波變換的硬閾值降噪方法和基于小波變換的軟閾值降噪方法能達到更好的降噪效果。最后研究了滾動軸承振動信號降噪問題的故障特征提取問題,介紹了基于Winger分布和奇異值分解相結合的特征提取方法,得到Winger時頻譜后,接著對Winger進行奇異值分解,在四種不同工況下分別得到特征向量,可以便于進一步開展針對滾動軸承振動信號降噪處理。
[Abstract]:Rolling bearings of large rotating machinery is the key equipment in the easy damage of rotating machinery parts, once the failure will lead to mechanical equipment running instability and a series of serious consequences, and the fault vibration signal extraction often shows the fluctuation characteristics of rolling bearing vibration signal contains the state information bearing running large, is one of the main basis to carry out fault analysis of bearing. Therefore, the rolling bearing vibration of large rotating machinery simulation signal and measured signal are respectively carried out the research of signal denoising method, the further processing of vibration signals to prevent safety accidents has important theoretical significance and practical value in engineering application. The main purpose of this thesis is the noise reduction of vibration signal of rolling bearing using large rotating machinery, vibration signal to extract needed. Needle The vibration signal characteristic of non-stationary signal denoising, the improved wavelet threshold denoising method based on semi soft. Feature extraction is the key step to realize mechanical fault diagnosis, and the rolling bearing fault diagnosis is the key. This paper firstly introduces the basic principle of wavelet analysis, this paper gives the definition of wavelet transform and basic introduces the basic properties of the common algorithm of wavelet transform is introduced. Then based on the traditional hard threshold denoising method based on wavelet transform and soft threshold denoising method based on wavelet transform, the analysis of advantages and disadvantages, on the basis of them, according to their disadvantages and deficiencies, proposed an improved semi soft threshold denoising method based on wavelet transform based on the transformation of the performance evaluation criterion. Finally, the noise reduction method is presented. Aiming at the problem of rolling bearing vibration and noise reduction based on wavelet transform semi soft threshold value by the improved Noise reduction methods are analyzed respectively the simulation test and practical test signal signal, finally the performance evaluation index, spectrum analysis compared the performance of the three methods, the results show that the proposed semi soft threshold denoising method based on wavelet transform than the traditional hard threshold denoising method based on wavelet transform and soft threshold denoising method based on wavelet transform based on can achieve better noise reduction effect. Finally the fault feature of vibration signal of rolling bearing extraction, based on the Winger distribution and the singular value decomposition combined feature extraction method, Winger spectrum, and singular value decomposition on Winger, under four different operating conditions are characteristic vector, can facilitate the further development of the rolling bearing vibration signal denoising.

【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TH133.33

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本文編號：1436017