本文關(guān)鍵詞：基于改進(jìn)提升小波的AE信號(hào)消噪及在故障診斷中的應(yīng)用研究　出處：《湖南科技大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 改進(jìn)提升小波 旋轉(zhuǎn)機(jī)械 聲發(fā)射 信號(hào)消噪 特征提取 故障診斷

【摘要】：旋轉(zhuǎn)機(jī)械是國(guó)民生產(chǎn)中的關(guān)鍵核心設(shè)備，對(duì)其進(jìn)行監(jiān)測(cè)與診斷，保障其安全、可靠運(yùn)行，對(duì)提高企業(yè)的經(jīng)濟(jì)效益、促進(jìn)整個(gè)國(guó)民經(jīng)濟(jì)的發(fā)展有著重要的作用。傳統(tǒng)的監(jiān)測(cè)與診斷一般基于振動(dòng)信號(hào)，而振動(dòng)信號(hào)分析法在強(qiáng)噪聲干擾背景下難以準(zhǔn)確獲取設(shè)備的故障特征信息�；诼暟l(fā)射(AE)信號(hào)的監(jiān)測(cè)與診斷方法具有靈敏度高、抗噪聲干擾能力強(qiáng)等優(yōu)點(diǎn)，更易于提取可表征設(shè)備運(yùn)行狀態(tài)的特征信息，將其應(yīng)用于旋轉(zhuǎn)機(jī)械故障診斷，可以有效的提高故障診斷的準(zhǔn)確率。 本文在分析以滾動(dòng)軸承為代表的旋轉(zhuǎn)機(jī)械聲發(fā)射信號(hào)特性的基礎(chǔ)上，針對(duì)傳統(tǒng)提升小波的不足，提出了一種適合于旋轉(zhuǎn)機(jī)械A(chǔ)E信號(hào)的改進(jìn)提升小波變換方法。研究了基于改進(jìn)提升小波的旋轉(zhuǎn)機(jī)械A(chǔ)E信號(hào)消噪方法，并結(jié)合經(jīng)驗(yàn)?zāi)B(tài)分解開(kāi)展AE信號(hào)特征提取、結(jié)合BP神經(jīng)網(wǎng)絡(luò)開(kāi)展AE信號(hào)故障識(shí)別研究，具體研究工作如下： (1)提出了旋轉(zhuǎn)機(jī)械A(chǔ)E信號(hào)提升小波改進(jìn)算法。通過(guò)理論分析，結(jié)合實(shí)驗(yàn)研究探明旋轉(zhuǎn)機(jī)械聲發(fā)射信號(hào)特性，在此基礎(chǔ)上提出改進(jìn)提升小波，包括分解過(guò)程中，引入局部判決函數(shù)，根據(jù)被分析AE信號(hào)的局部特征自適應(yīng)的構(gòu)造更新算子；利用遺傳算法，，對(duì)預(yù)測(cè)算子進(jìn)行優(yōu)化。重構(gòu)過(guò)程中，利用自適應(yīng)閾值消噪對(duì)每一層的AE信號(hào)進(jìn)行消噪，提高了提升小波的自適應(yīng)消噪性能。 (2)研究了基于改進(jìn)提升小波的旋轉(zhuǎn)機(jī)械A(chǔ)E信號(hào)消噪方法。針對(duì)自適應(yīng)閾值消噪公式中參數(shù)p的取值過(guò)大或過(guò)小都會(huì)影響消噪的效果，通過(guò)AE仿真信號(hào)研究了改進(jìn)提升小波在AE信號(hào)消噪中的最佳參數(shù)值，并用實(shí)測(cè)的AE信號(hào)驗(yàn)證了改進(jìn)提升小波比傳統(tǒng)提升小波和小波變換具有更好的消噪性能。 (3)采用改進(jìn)提升小波和經(jīng)驗(yàn)?zāi)B(tài)分解對(duì)旋轉(zhuǎn)機(jī)械故障的AE信號(hào)進(jìn)行了特征提取研究。利用改進(jìn)提升小波對(duì)經(jīng)驗(yàn)?zāi)B(tài)分解的AE信號(hào)進(jìn)行消噪處理，然后用相關(guān)函數(shù)法求出經(jīng)驗(yàn)?zāi)B(tài)分解后的有效內(nèi)稟模態(tài)函數(shù)，并進(jìn)行包絡(luò)解調(diào)分析，能夠準(zhǔn)確提取旋轉(zhuǎn)機(jī)械A(chǔ)E信號(hào)中的故障特征信息。 (4)結(jié)合改進(jìn)提升小波和BP神經(jīng)網(wǎng)絡(luò)對(duì)滾動(dòng)軸承進(jìn)行了故障識(shí)別研究。將改進(jìn)提升小波作為BP神經(jīng)網(wǎng)絡(luò)的前處理器，對(duì)AE信號(hào)進(jìn)行消噪預(yù)處理，提取消噪后AE信號(hào)的特征參數(shù)作為BP神經(jīng)網(wǎng)絡(luò)的輸入量，減少了BP神經(jīng)網(wǎng)絡(luò)的訓(xùn)練步驟，提高了軸承故障識(shí)別的準(zhǔn)確率。
[Abstract]:The rotating machinery is the key core equipment in the national production . It plays an important role in monitoring and diagnosing it , ensuring the safety and reliable operation of the enterprise . The traditional monitoring and diagnosis is based on the vibration signal , and the vibration signal analysis method has the advantages of high sensitivity and strong anti - noise interference . It is easier to extract the characteristic information which can characterize the running state of the equipment . It can be applied to the fault diagnosis of rotating machinery , which can effectively improve the accuracy of fault diagnosis . On the basis of analyzing the characteristics of the rotating machinery acoustic emission signal represented by the rolling bearing , this paper presents an improved wavelet transform method which is suitable for the rotating mechanical AE signal . Based on the improvement of the wavelet transform method , the AE signal feature extraction is carried out in combination with the empirical mode decomposition , and the AE signal feature extraction is carried out in combination with the BP neural network . The specific research work is as follows : ( 1 ) An improved algorithm for improving the AE signal of a rotating machinery is proposed . Based on the theoretical analysis , the characteristics of the acoustic emission signal of the rotating machinery are investigated by combining the experimental study . On the basis of this , an improved wavelet is proposed , which includes the introduction of a local decision function in the decomposition process , the optimization of the predictive operator based on the local feature of the analyzed AE signal . In the reconstruction process , the adaptive threshold de - noising is used to de - noising each layer of AE signal , which improves the adaptive noise elimination performance of the lifting wavelet . ( 2 ) The method of improving the signal denoising based on the modified lifting wavelet is studied . The optimal parameter value of the wavelet in the noise elimination of AE signal is studied by AE simulation signal . The best parameter value of the wavelet in the noise elimination of AE signal is studied through AE simulation signal . The improved wavelet and wavelet transform are improved with the measured AE signal to improve the noise elimination performance . ( 3 ) An improved wavelet and empirical mode decomposition is used to extract the AE signal of the rotating machinery fault . An improved wavelet is used to denoise the AE signal decomposed by the empirical mode , then the effective intrinsic mode function after the empirical mode decomposition is obtained by the correlation function method , and the envelope demodulation analysis is carried out to accurately extract the fault feature information in the rotating mechanical AE signal . ( 4 ) Combining the improved lifting wavelet and BP neural network to study the fault identification of the rolling bearing , the improved lifting wavelet is used as the front processor of the BP neural network , the noise elimination preprocessing is carried out on the AE signal , the characteristic parameter of the AE signal after canceling noise is taken as the input amount of the BP neural network , the training step of the BP neural network is reduced , and the accuracy of the bearing fault identification is improved .

【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TN911.4;TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 高立新;湯文亮;胥永剛;殷海晨;;基于冗余第二代小波的降噪技術(shù)[J];北京工業(yè)大學(xué)學(xué)報(bào);2008年12期

2 高立新;殷海晨;張建宇;胥永剛;;第二代小波分析在軸承故障診斷中的應(yīng)用[J];北京工業(yè)大學(xué)學(xué)報(bào);2009年05期

3 高立新;吳麗娟;張建宇;;基于EMD解調(diào)方法的齒輪早期故障診斷[J];北京工業(yè)大學(xué)學(xué)報(bào);2009年07期

4 張立保,王珂;零樹(shù)框架下整數(shù)小波圖像編碼的改進(jìn)[J];電路與系統(tǒng)學(xué)報(bào);2003年03期

5 李成;李平;宋執(zhí)環(huán);;一種提升格式的非線性插值小波圖像編碼方法[J];電路與系統(tǒng)學(xué)報(bào);2008年01期

6 馬建倉(cāng);彭錦濤;孟凡路;;自適應(yīng)提升小波變換及飛機(jī)發(fā)動(dòng)機(jī)故障分析[J];計(jì)算機(jī)工程與應(yīng)用;2010年18期

7 葉重元;黃永東;;小波閾值去噪算法的新改進(jìn)[J];計(jì)算機(jī)工程與應(yīng)用;2011年12期

8 陳珍萍;歐陽(yáng)名三;劉淮霞;;雙自適應(yīng)提升小波變換在PQD去噪中的應(yīng)用[J];計(jì)算機(jī)工程與應(yīng)用;2011年16期

9 劉樹(shù)春;潘紫微;宋淼;;第二代小波在振動(dòng)信號(hào)去噪中新方法的研究[J];機(jī)械傳動(dòng);2008年03期

10 段晨東,李凌均,何正嘉;第二代小波變換在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[J];機(jī)械科學(xué)與技術(shù);2004年02期

相關(guān)博士學(xué)位論文 前2條

1 孫軼;基于自適應(yīng)提升小波的信號(hào)去噪技術(shù)研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2008年

2 陳孝敬;小波變換在光譜和多光譜圖像的應(yīng)用與研究[D];廈門(mén)大學(xué);2009年

相關(guān)碩士學(xué)位論文 前7條

1 馬東宇;基于Gaussian型RBF神經(jīng)網(wǎng)絡(luò)的函數(shù)逼近與應(yīng)用[D];中南大學(xué);2011年

2 印欣運(yùn);聲發(fā)射技術(shù)在旋轉(zhuǎn)機(jī)械碰摩故障診斷中的應(yīng)用[D];清華大學(xué);2005年

3 時(shí)麗娜;面向故障診斷的傳感器布局和微弱信號(hào)檢測(cè)方法的研究[D];上海交通大學(xué);2007年

4 周潔;裂紋聲發(fā)射源定位研究及發(fā)展趨勢(shì)預(yù)估初探[D];廣西大學(xué);2007年

5 王玉雷;基于粒子群優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)的柴油機(jī)故障診斷研究[D];中北大學(xué);2008年

6 張維新;基于小波分析與神經(jīng)網(wǎng)絡(luò)的軸承故障診斷研究[D];天津大學(xué);2008年

7 湯文亮;冗余提升小波分析在故障診斷中的研究與應(yīng)用[D];北京工業(yè)大學(xué);2010年

本文編號(hào)：1416513