本文關(guān)鍵詞：滾動(dòng)軸承振動(dòng)信號(hào)降噪方法研究　出處：《華北電力大學(xué)(北京)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 小波變換 振動(dòng) 軸承 降噪處理

【摘要】：大型旋轉(zhuǎn)機(jī)械設(shè)備中的滾動(dòng)軸承是旋轉(zhuǎn)機(jī)械中關(guān)鍵且容易損壞的部件,旦出現(xiàn)故障,將會(huì)導(dǎo)致機(jī)械設(shè)備運(yùn)行失穩(wěn)等一系列嚴(yán)重后果,而故障部位提取的振動(dòng)信號(hào)往往表現(xiàn)出波動(dòng)特征。滾動(dòng)軸承振動(dòng)信號(hào)包含了大量的軸承運(yùn)行狀態(tài)信息,是對(duì)軸承開(kāi)展故障分析的主要依據(jù)之一。因此,論文針對(duì)大型旋轉(zhuǎn)機(jī)械的滾動(dòng)軸承振動(dòng)的仿真信號(hào)和實(shí)測(cè)信號(hào)分別開(kāi)展了信號(hào)降噪方法研究,將對(duì)進(jìn)一步處理振動(dòng)監(jiān)測(cè)信號(hào)、預(yù)防安全事故發(fā)生都具有重要的理論指導(dǎo)意義和工程實(shí)際應(yīng)用價(jià)值。論文研究的主要目的是利用大型旋轉(zhuǎn)機(jī)械設(shè)備滾動(dòng)軸承振動(dòng)信號(hào)進(jìn)行降噪處理,提取出所需要的振動(dòng)信號(hào)。針對(duì)振動(dòng)信號(hào)非平穩(wěn)的特點(diǎn),信號(hào)降噪研究了基于改進(jìn)的小波半軟閡值降噪方法。特征提取是實(shí)現(xiàn)機(jī)械故障診斷的關(guān)鍵步驟,對(duì)與滾動(dòng)軸承振動(dòng)故障診斷至為關(guān)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(zhì),介紹了常見(jiàn)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(diǎn)和缺點(diǎn)基、礎(chǔ)上,針對(duì)它們存在的缺點(diǎn)和不足,提出了改進(jìn)的基于小波變換的半軟閾值降噪方法。最后給出了評(píng)價(jià)各降噪方法的性能評(píng)價(jià)準(zhǔn)則。針對(duì)滾動(dòng)軸承振動(dòng)降噪問(wèn)題采用改進(jìn)的基于小波變換的半軟閾值降噪方法分別進(jìn)行了仿真信號(hào)測(cè)試和實(shí)際信號(hào)測(cè)試分析,最后通過(guò)性能評(píng)價(jià)指標(biāo)、頻譜分析對(duì)比了這三種方法的性能優(yōu)劣,結(jié)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(dá)到更好的降噪效果。最后研究了滾動(dòng)軸承振動(dòng)信號(hào)降噪問(wèn)題的故障特征提取問(wèn)題,介紹了基于Winger分布和奇異值分解相結(jié)合的特征提取方法,得到Winger時(shí)頻譜后,接著對(duì)Winger進(jìn)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(jìn)一步開(kāi)展針對(duì)滾動(dòng)軸承振動(dòng)信號(hào)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(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH133.33

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王衍學(xué);何正嘉;訾艷陽(yáng);袁靜;;基于LMD的時(shí)頻分析方法及其機(jī)械故障診斷應(yīng)用研究[J];振動(dòng)與沖擊;2012年09期

2 王冬云;張文志;;基于小波包變換的滾動(dòng)軸承故障診斷[J];中國(guó)機(jī)械工程;2012年03期

3 張超;陳建軍;徐亞蘭;;基于EMD分解和奇異值差分譜理論的軸承故障診斷方法[J];振動(dòng)工程學(xué)報(bào);2011年05期

4 胡愛(ài)軍;孫敬敬;向玲;;經(jīng)驗(yàn)?zāi)B(tài)分解中的模態(tài)混疊問(wèn)題[J];振動(dòng).測(cè)試與診斷;2011年04期

5 蔡艷平;李艾華;石林鎖;白向峰;沈金偉;;基于EMD與譜峭度的滾動(dòng)軸承故障檢測(cè)改進(jìn)包絡(luò)譜分析[J];振動(dòng)與沖擊;2011年02期

6 朱永年;趙君愛(ài);;小波—BP神經(jīng)網(wǎng)絡(luò)在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[J];電子機(jī)械工程;2011年01期

7 張祖德;王玉強(qiáng);;旋轉(zhuǎn)機(jī)械轉(zhuǎn)子不對(duì)中的故障診斷[J];特鋼技術(shù);2010年04期

8 趙學(xué)智;葉邦彥;陳統(tǒng)堅(jiān);;多分辨奇異值分解理論及其在信號(hào)處理和故障診斷中的應(yīng)用[J];機(jī)械工程學(xué)報(bào);2010年20期

9 祁克玉;施坤林;霍鵬飛;何正嘉;;EMD端點(diǎn)效應(yīng)處理在轉(zhuǎn)子摩擦故障診斷中的應(yīng)用[J];振動(dòng).測(cè)試與診斷;2010年05期

10 陳永會(huì);姜旭;郭山國(guó);李海虹;;基于小波分析和Hilbert變換的滾動(dòng)軸承故障診斷[J];機(jī)械設(shè)計(jì);2010年08期

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