【摘要】：針對(duì)大型風(fēng)力發(fā)電機(jī)組齒輪傳動(dòng)系統(tǒng)容易出現(xiàn)故障的特點(diǎn),對(duì)其進(jìn)行故障診斷中故障特征提取方法和故障識(shí)別方法進(jìn)行了研究。簡(jiǎn)單介紹了風(fēng)力發(fā)電機(jī)組成和工作機(jī)理,重點(diǎn)介紹了齒輪傳動(dòng)系統(tǒng)的組成和常見故障,列出了風(fēng)力發(fā)電機(jī)組齒輪傳動(dòng)系統(tǒng)故障特征頻率的計(jì)算公式,介紹了引起齒輪振動(dòng)的原因,簡(jiǎn)述了齒輪箱振動(dòng)信號(hào)的特點(diǎn)。利用實(shí)驗(yàn)采集的原始振動(dòng)加速度信號(hào)對(duì)時(shí)域統(tǒng)計(jì)指標(biāo)進(jìn)行了計(jì)算,根據(jù)時(shí)域統(tǒng)計(jì)指標(biāo)的方差可表示不同狀態(tài)的離散程度,指出了時(shí)域統(tǒng)計(jì)指標(biāo)中可以作為故障特征元素的指標(biāo)。利用幅值譜和細(xì)化譜分析方法對(duì)各故障狀態(tài)下的頻域特征進(jìn)行了分析,說明了各故障狀態(tài)下信號(hào)調(diào)制的邊頻帶特點(diǎn)。通過對(duì)風(fēng)力發(fā)電機(jī)齒輪傳動(dòng)系統(tǒng)故障狀態(tài)振動(dòng)信號(hào)的時(shí)域特征和頻域特征分析,幫助我們了解故障特點(diǎn)和故障產(chǎn)生原因,為下一步故障特征提取提供指導(dǎo)和依據(jù)。針對(duì)經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)方法用于齒輪故障診斷的優(yōu)越性和不足,對(duì)集合經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)可以減小模態(tài)混疊效應(yīng)的觀點(diǎn)進(jìn)行了仿真驗(yàn)證,提出了該方法中兩個(gè)主要參數(shù)的確定方法。運(yùn)用相關(guān)系數(shù)法對(duì)集合經(jīng)驗(yàn)?zāi)B(tài)分解得到的內(nèi)稟模態(tài)函數(shù)(IMF)分量進(jìn)行了篩選,計(jì)算了篩選后的有意義的IMF分量的能量和占總能量的能量比,構(gòu)造故障特征向量。根據(jù)現(xiàn)有的灰色關(guān)聯(lián)度算法和缺陷提出了改進(jìn)的灰色相似關(guān)聯(lián)度算法,將改進(jìn)的灰色相似關(guān)聯(lián)度算法用于風(fēng)力發(fā)電機(jī)組齒輪傳動(dòng)系統(tǒng)的故障分類識(shí)別,實(shí)驗(yàn)驗(yàn)證了其有效性,并與多分類支持向量機(jī)方法做了比較,結(jié)果證明灰色相似關(guān)聯(lián)度算法的準(zhǔn)確性更好,實(shí)時(shí)性更高。
[Abstract]:In view of the characteristic that the gear transmission system of large wind turbine is prone to failure, the method of fault feature extraction and fault identification in fault diagnosis is studied. This paper briefly introduces the composition and working mechanism of wind turbine, emphasizes on the composition and common faults of gear transmission system, and lists the formula for calculating the characteristic frequency of gear transmission system of wind turbine. The causes of gear vibration are introduced, and the characteristics of gear box vibration signal are briefly described. The time-domain statistical index is calculated by using the original vibration acceleration signal collected by the experiment. According to the variance of the time-domain statistical index, the discrete degree of different states can be expressed, and the time-domain statistical index can be used as the index of fault characteristic element. The frequency domain characteristics of each fault state are analyzed by means of amplitude spectrum and thinning spectrum analysis method, and the edge band characteristics of signal modulation in each fault state are explained. By analyzing the time-domain and frequency-domain characteristics of the vibration signals in the fault state of the gear transmission system of the wind turbine, this paper helps us to understand the fault characteristics and the causes of the faults, and provides guidance and basis for the next step of the fault feature extraction. In view of the advantages and disadvantages of the empirical mode decomposition (EMD) method for gear fault diagnosis, the viewpoint that the set empirical mode decomposition (EEMD) can reduce the modal aliasing effect is verified by simulation. A method for determining two main parameters of this method is presented. The intrinsic mode function (IMF) components obtained from the empirical mode decomposition of the set are screened by the correlation coefficient method. The energy and the energy ratio of the significant IMF component to the total energy are calculated, and the fault eigenvector is constructed. According to the existing grey correlation degree algorithm and the defects, the improved grey similar correlation degree algorithm is proposed. The improved grey similar correlation degree algorithm is applied to the fault classification and identification of the wind turbine gear transmission system, and the effectiveness of the algorithm is verified by experiments. Compared with the multi-classification support vector machine method, the results show that the grey similarity correlation algorithm has better accuracy and higher real-time performance.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM315;TH132.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 郝旺身;王洪明;董辛e，

本文編號(hào)：2346046