本文選題：風(fēng)電機(jī)組 + 盲分離　； 參考：《沈陽(yáng)工業(yè)大學(xué)》2013年博士論文

【摘要】：由于大型風(fēng)力發(fā)電機(jī)組工作的條件比較惡劣，而且運(yùn)行時(shí)通常不是長(zhǎng)時(shí)間穩(wěn)定地處于一種載荷工況，而是隨著風(fēng)、電網(wǎng)、溫度等條件的變化而不斷的進(jìn)行調(diào)整，因此機(jī)組的傳動(dòng)鏈所傳遞的載荷是不斷變化的，這就會(huì)對(duì)傳動(dòng)鏈上的各個(gè)零部件提出一定的可靠性要求：一是零部件質(zhì)量的可靠性，二是當(dāng)零部件出現(xiàn)早期損傷時(shí)，能夠及時(shí)的發(fā)現(xiàn)，以便作到故障的早發(fā)現(xiàn)早處理。對(duì)于第一點(diǎn)與設(shè)計(jì)和制造有關(guān)，本文不作討論，這里只討論第二種情況。 當(dāng)風(fēng)電機(jī)組出現(xiàn)故障時(shí)，故障零部件通常會(huì)產(chǎn)生具有一定特征的振動(dòng)信號(hào)。但是在故障初期，這種故障特征并不明顯。同時(shí)由于在風(fēng)機(jī)運(yùn)行時(shí)，許多零部件都會(huì)發(fā)出振動(dòng)和噪音，振動(dòng)傳感器拾取信息時(shí)難免會(huì)受到強(qiáng)信號(hào)和噪聲信號(hào)的影響,例如潤(rùn)滑和散熱系統(tǒng)的運(yùn)作、偏航和變槳機(jī)構(gòu)的動(dòng)作、電氣系統(tǒng)的運(yùn)行和發(fā)電機(jī)的勵(lì)磁振動(dòng)等，這些強(qiáng)信號(hào)和噪音之間還會(huì)互相干擾形成復(fù)雜的背景噪音，使早期故障特征振動(dòng)信號(hào)湮沒(méi)于背景噪音，提取真實(shí)準(zhǔn)確的信息比較困難。同時(shí)，盲源分離算法對(duì)噪聲很敏感，當(dāng)利用該算法直接對(duì)混疊信號(hào)進(jìn)行分離時(shí)，會(huì)造成很大的誤差或得出錯(cuò)誤的結(jié)論。因此，對(duì)采集到的振動(dòng)信號(hào)進(jìn)行盲分離前的強(qiáng)信號(hào)的去除以及降噪，對(duì)提高信噪比就顯得尤為重要。 本文采用自相關(guān)方法和EEMD方法對(duì)采集的信號(hào)進(jìn)行降噪；采用擴(kuò)展多虛擬通道FastIca技術(shù)進(jìn)行強(qiáng)信號(hào)分離；針對(duì)診斷領(lǐng)域中的單通道信號(hào)難以應(yīng)用盲源分離方法的難點(diǎn)，采用EEMD-FastIca技術(shù)，可以滿(mǎn)足盲源分離（BSS）算法的多入多出（MIMO）條件，實(shí)現(xiàn)信號(hào)的盲分離。這種方法的優(yōu)點(diǎn)是既不必先知道源信號(hào)的數(shù)量，，也不必先了解信號(hào)的產(chǎn)生和傳遞的參數(shù)，就能實(shí)現(xiàn)采集的信號(hào)中的各數(shù)據(jù)得盲分離；該方法可以提取風(fēng)電機(jī)組傳動(dòng)鏈中的早期信號(hào)特征，提高了診斷的效率和準(zhǔn)確性。為了驗(yàn)證方法的有效性，本文先通過(guò)仿真實(shí)驗(yàn)?zāi)M出風(fēng)電機(jī)組機(jī)械系統(tǒng)中的典型振動(dòng)信號(hào)，并用上述方法分別進(jìn)行分析測(cè)試，以確定其可以有效地分離信號(hào)。 機(jī)組的增速箱分離出特征振動(dòng)信號(hào)；通過(guò)對(duì)佳木斯風(fēng)電場(chǎng)采集的數(shù)據(jù)進(jìn)行分析，診斷和分析一臺(tái)1.5MW級(jí)風(fēng)力發(fā)電機(jī)組的軸流風(fēng)機(jī)散熱器的早期故障信號(hào)，驗(yàn)證該方法在風(fēng)電機(jī)組振動(dòng)信號(hào)處理的有效性和基于EEMD-FastIca算法和強(qiáng)信號(hào)去除的虛擬通道盲分離方法及其擴(kuò)展算法適用風(fēng)電機(jī)組信號(hào)的處理、預(yù)測(cè)機(jī)械系統(tǒng)的早期故障。
[Abstract]:Because the working conditions of large-scale wind turbines are relatively bad, and the operation time is usually not in a load condition for a long time and stable, but with the changes of wind, power grid, temperature and other conditions, they are constantly adjusted. Therefore, the load transmitted by the transmission chain of the unit is constantly changing, which will put forward certain reliability requirements for each component on the transmission chain: first, the reliability of the parts quality; second, when the parts are damaged early, Be able to find out in time, in order to make the fault early detection and early processing. The first point is related to design and manufacture, which is not discussed in this paper. Only the second case is discussed here. When wind turbine failure occurs, fault components usually produce vibration signals with certain characteristics. However, at the beginning of the fault, the characteristics of the fault are not obvious. At the same time, since many parts and components will emit vibration and noise when the fan is running, the vibration sensor will inevitably be affected by strong signals and noise signals when picking up information, such as the operation of lubrication and heat dissipation systems, the action of yaw and propeller mechanism. With the operation of electrical system and excitation vibration of generator, these strong signals will interfere with each other to form complex background noise, which makes the vibration signal of early fault feature obliterate in the background noise, so it is difficult to extract the true and accurate information. At the same time, the blind source separation algorithm is sensitive to noise, when it is used to separate the aliasing signal directly, it will cause great error or get the wrong conclusion. Therefore, the removal of strong signals and noise reduction before blind separation of the collected vibration signals is particularly important to improve the signal-to-noise ratio (SNR). In this paper, the autocorrelation method and EEMD method are used to reduce the noise of the collected signal; the extended multi-virtual channel FastIca technique is used for strong signal separation; and the EEMD-FastIca technology is adopted to solve the difficulty of blind source separation method in the single channel signal in the diagnosis field. Blind source separation (BSS) algorithm can satisfy the multi-input and multi-output MIMO-conditions and realize blind signal separation. The advantage of this method is that it can achieve blind separation of each data in the collected signal without first knowing the number of the source signal and the parameters of the signal generation and transmission. This method can extract the early signal features from the transmission chain of wind turbine, and improve the efficiency and accuracy of diagnosis. In order to verify the effectiveness of the method, the typical vibration signals in the wind turbine mechanical system are simulated by simulation experiments, and the above methods are used to analyze and test respectively to determine that the signals can be separated effectively. By analyzing the data collected from Jiamusi wind farm, the early fault signals of the axial fan radiator of a 1.5MW wind turbine are diagnosed and analyzed. The validity of this method in wind turbine vibration signal processing and the blind separation method of virtual channel based on EEMD-FastIca algorithm and strong signal removal and its extended algorithm are proved to be applicable to wind turbine signal processing and early fault prediction of mechanical system.
【學(xué)位授予單位】：沈陽(yáng)工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TM315;TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 李士心,劉魯源;小波域中值濾波器設(shè)計(jì)的研究[J];電子科技大學(xué)學(xué)報(bào);2003年01期

2 潘泉;孟晉麗;張磊;程詠梅;張洪才;;小波濾波方法及應(yīng)用[J];電子與信息學(xué)報(bào);2007年01期

3 孟濤,廖明夫,李輝;齒輪故障診斷的時(shí)延相關(guān)解調(diào)法[J];航空動(dòng)力學(xué)報(bào);2003年01期

4 劉振興;尉宇;趙敏;陳正澎;;基于RELAX頻譜分析方法的鼠籠式異步電動(dòng)機(jī)轉(zhuǎn)子故障診斷[J];中國(guó)電機(jī)工程學(xué)報(bào);2006年22期

相關(guān)博士學(xué)位論文 前5條

1 單光坤;兆瓦級(jí)風(fēng)電機(jī)組狀態(tài)監(jiān)測(cè)及故障診斷研究[D];沈陽(yáng)工業(yè)大學(xué);2011年

2 劉佳;單通道盲源分離及其在水聲信號(hào)處理中的應(yīng)用研究[D];哈爾濱工程大學(xué);2011年

3 何慧龍;機(jī)電設(shè)備微弱特征提取與診斷方法研究[D];天津大學(xué);2007年

4 周勃;基于盲分離的空調(diào)機(jī)組故障振聲診斷研究[D];沈陽(yáng)工業(yè)大學(xué);2008年

5 徐春生;微弱信號(hào)檢測(cè)及機(jī)械故障診斷系統(tǒng)研究[D];天津大學(xué);2008年

本文編號(hào)：1906597