發(fā)布時(shí)間：2018-01-26 11:06

  本文關(guān)鍵詞： 風(fēng)電機(jī)組 發(fā)電機(jī) 齒輪箱 故障診斷 頻譜分析法 主成分遺傳神經(jīng)網(wǎng)絡(luò)　出處：《華北電力大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：風(fēng)電建設(shè)快速推進(jìn)的同時(shí),也帶來了一系列的挑戰(zhàn),突出的表現(xiàn)就是風(fēng)電機(jī)組的質(zhì)量問題頻發(fā),嚴(yán)重影響正常的生產(chǎn)發(fā)電工作,造成巨大的經(jīng)濟(jì)損失。因此,加強(qiáng)風(fēng)電機(jī)組故障診斷,對(duì)降低風(fēng)電場(chǎng)維護(hù)費(fèi)用,提高風(fēng)電場(chǎng)運(yùn)行經(jīng)濟(jì)效益具有重要意義。風(fēng)電機(jī)組主要包括齒輪箱、發(fā)電機(jī)、葉片、液壓系統(tǒng)和偏航系統(tǒng)等部件,齒輪箱和發(fā)電機(jī)是關(guān)鍵部件,也是故障發(fā)生率最高的部件,其運(yùn)行的穩(wěn)定性會(huì)影響到整機(jī)性能。故本文以風(fēng)電機(jī)組發(fā)電機(jī)和齒輪箱為研究對(duì)象,對(duì)其故障診斷方法進(jìn)行了研究。 首先,考慮到發(fā)電機(jī)軸承故障振動(dòng)響應(yīng)較弱,本文提出了倒頻譜域相干分析的發(fā)電機(jī)組軸承故障特征提取方法,該方法利用相干分析減弱測(cè)量信號(hào)中噪聲的干擾,突出故障信息,然后對(duì)相干函數(shù)做倒頻域計(jì)算,提取邊帶特征。針對(duì)齒輪箱齒輪故障振動(dòng)信號(hào)頻譜結(jié)構(gòu)的特點(diǎn),提出了基于小波包與倒頻譜分析的風(fēng)電機(jī)組齒輪箱齒輪裂紋診斷方法,兩種方法使風(fēng)電機(jī)組發(fā)電機(jī)軸承故障和齒輪箱齒輪故障診斷通過簡(jiǎn)單易行的頻譜分析實(shí)現(xiàn)。 然后,本文提出了同類信息融合的方法,該方法選取振動(dòng)信號(hào)的峭度、峰值作為時(shí)域特征值,利用小波包算法提取頻帶能量和二范數(shù)作為時(shí)頻域特征值�？紤]到特征值之間的相關(guān)性,利用主成分分析法確定主成分,從而減少神經(jīng)網(wǎng)絡(luò)的輸入變量。利用遺傳算法對(duì)BP神經(jīng)網(wǎng)絡(luò)權(quán)值和偏置進(jìn)行優(yōu)化,建立遺傳神經(jīng)網(wǎng)絡(luò)的故障診斷模型。仿真測(cè)試表明了算法的有效性。 最后,提出了異類信息融合的方法,該方法針對(duì)風(fēng)電機(jī)組齒輪箱單一故障信號(hào)的局限性和故障特征存在較強(qiáng)非線性關(guān)系的特點(diǎn),以采集的振動(dòng)信號(hào)、溫度信號(hào)和潤(rùn)滑油信號(hào)為原始信源,分別提取它們的峭度、小波包頻帶能量,齒輪箱軸承溫度、齒輪箱油溫,潤(rùn)滑油粘度作為特征值�？紤]到特征值之間的相關(guān)性,利用主成分分析法對(duì)原始特征值的組合進(jìn)行降維融合,得到信息互補(bǔ)的特征量。將融合特征通過遺傳算法優(yōu)化的神經(jīng)網(wǎng)絡(luò)進(jìn)行模式識(shí)別。仿真測(cè)試表明了該方法比同類信息特征融合法具有更高的診斷精度。
[Abstract]:At the same time, wind power construction has brought a series of challenges, the outstanding performance is the frequent occurrence of wind turbine quality problems, seriously affect the normal production and power generation work, resulting in huge economic losses. Strengthening the fault diagnosis of wind turbine is of great significance to reduce the maintenance cost of wind farm and improve the economic benefit of wind farm operation. The wind turbine mainly includes gearbox, generator and blade. Components such as hydraulic system and yaw system, gearbox and generator are the key components, and also the components with the highest fault rate. The stability of its operation will affect the performance of the whole machine. Therefore, the fault diagnosis method of the generator and gearbox of wind turbine is studied in this paper. Firstly, considering the weak vibration response of generator bearing fault, this paper proposes a method of feature extraction of generator bearing fault based on coherent analysis in cepstrum domain. In this method, the interference of noise in the measured signal is reduced by coherence analysis, and the fault information is highlighted, then the coherent function is calculated in inverted frequency domain. According to the characteristic of frequency spectrum of gear fault vibration signal of gear box, a method of gear crack diagnosis based on wavelet packet and cepstrum analysis is proposed. The two methods make the fault diagnosis of generator bearing and gearbox gear by simple and easy spectrum analysis. Then, a similar information fusion method is proposed, in which the kurtosis of the vibration signal is selected and the peak value is taken as the time domain eigenvalue. Wavelet packet algorithm is used to extract frequency band energy and two-norm as time-frequency domain eigenvalues. Considering the correlation between eigenvalues, principal component analysis is used to determine principal components. In order to reduce the input variables of neural network, the weight and bias of BP neural network are optimized by genetic algorithm, and the fault diagnosis model of genetic neural network is established. The simulation results show that the algorithm is effective. Finally, a method of heterogeneous information fusion is proposed, which aims at the limitation of the single fault signal of the gearbox of wind turbine and the strong nonlinear relationship between the fault characteristics, so as to collect the vibration signal. The temperature signal and the lubricating oil signal are the original information sources, and their kurtosis, wavelet packet frequency band energy, gearbox bearing temperature, gear box oil temperature are extracted respectively. The viscosity of lubricating oil is regarded as the eigenvalue. Considering the correlation between the eigenvalues, the combination of the original eigenvalues is reduced by the principal component analysis (PCA). The information complementary feature quantity is obtained. The fusion feature is recognized by the neural network optimized by genetic algorithm. The simulation results show that this method has higher diagnostic accuracy than the similar information feature fusion method.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM315

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