本文選題：風電機組軸承 + 故障診斷�。� 參考：《新疆大學》2017年碩士論文

【摘要】：隨著全球風力發(fā)電行業(yè)快速發(fā)展,風電機組運維和故障診斷市場需求逐步增加,而風電機組軸承作為風電機組關鍵部件之一,其正常、穩(wěn)定運行直接影響著風電機組能量轉(zhuǎn)化率和機組其他部件健康狀態(tài)。振動監(jiān)測是目前軸承狀態(tài)監(jiān)測故障診斷的常用方法,風電機組數(shù)量多、振動測點多、采樣率高造成數(shù)據(jù)量非常大,達到PB甚至TB,給數(shù)據(jù)傳輸、分析和診斷提出了挑戰(zhàn)。隨著互聯(lián)網(wǎng)快速發(fā)展,各種大數(shù)據(jù)、云計算分析和處理新方法、新技術出現(xiàn),大數(shù)據(jù)分析主要基于小數(shù)據(jù)的探索。因此,本文提出了集成經(jīng)驗模態(tài)分解與峭度系數(shù)和相關系數(shù)的關聯(lián)度提取方法,通過時域參數(shù)、AR模型參數(shù)、能量熵參數(shù)提取了軸承故障和正常軸承之間的特征值矩陣,將特征值輸入徑向基核函數(shù)的支持向量機,訓練故障嚴重程度的診斷模型,通過實驗室軸承數(shù)據(jù)和風電機組實際運行軸承數(shù)據(jù),驗證了模型故障識別的準確率。通過對部分數(shù)據(jù)探索和研究,提出了風電機組軸承故障診斷云端化,運用亞馬遜提供的AWS云計算平臺,搭建基于AWS的風電機組軸承故障診斷研究平臺,將采用的分析方法向云端進行算法的并行化和遷移,主要通過Python開發(fā)語言實現(xiàn)了多風電場多臺機組振動信號實時信號采集、傳輸和處理,同時,針對每臺風電機組軸承振動信號歷史數(shù)據(jù)定期進行批處理,將其故障診斷和識別模型迭代和更新,實現(xiàn)了風機主軸承故障診斷專業(yè)化和定制化,驗證和實現(xiàn)了風電機組故障診斷與云計算技術結合,對風電機組運維和故障診斷等領域具有較強的指導意義和參考價值。
[Abstract]:With the rapid development of the global wind power industry, the market demand for wind turbine operation and fault diagnosis is gradually increasing. As one of the key components of wind turbine, the bearing of wind turbine is normal. Stable operation directly affects the energy conversion rate of wind turbine and the health state of other components of wind turbine. Vibration monitoring is a commonly used method for fault diagnosis of bearing condition monitoring at present. The large amount of data caused by the large number of wind turbine units, the large number of vibration measuring points and the high sampling rate lead to the achievement of PB or even TB, which poses a challenge to data transmission, analysis and diagnosis. With the rapid development of the Internet, all kinds of big data, cloud computing analysis and processing new methods, new technologies appear, big data analysis is mainly based on the exploration of small data. Therefore, this paper presents a method of extracting correlation degree by integrating empirical mode decomposition with kurtosis coefficient and correlation coefficient. The eigenvalue matrix between bearing fault and normal bearing is extracted by time domain parameter AR model parameter and energy entropy parameter. The eigenvalue is input into the support vector machine of radial basis function and the diagnosis model of fault severity is trained. The accuracy of fault identification of the model is verified by the laboratory bearing data and the actual running bearing data of wind turbine. Through the exploration and research of some data, the paper puts forward the cloud diagnosis of wind turbine bearing fault, and builds the research platform of wind turbine bearing fault diagnosis based on AWS by using the AWS cloud computing platform provided by Amazon. The algorithm is parallelized and migrated to the cloud, and the real-time signal acquisition, transmission and processing of vibration signal of multi-wind farm and multi-unit are realized by Python development language, at the same time, According to the historical data of bearing vibration signal of each wind turbine unit, batch processing is carried out periodically, and the fault diagnosis and identification model is iterated and updated to realize the specialization and customization of fault diagnosis of main bearing of fan. The combination of wind turbine fault diagnosis and cloud computing technology is verified and realized, which has strong guiding significance and reference value for wind turbine operation and fault diagnosis.
【學位授予單位】：新疆大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315

【參考文獻】

相關期刊論文 前10條

1 薛禹勝;賴業(yè)寧;;大能源思維與大數(shù)據(jù)思維的融合(一)大數(shù)據(jù)與電力大數(shù)據(jù)[J];電力系統(tǒng)自動化;2016年01期

2 王相偉;史玉良;張建林;梁波;程翠萍;;基于Hadoop的用電信息大數(shù)據(jù)計算服務及應用[J];電網(wǎng)技術;2015年11期

3 宋亞奇;周國亮;朱永利;李莉;王德文;;云平臺下并行總體經(jīng)驗模態(tài)分解局部放電信號去噪方法[J];電工技術學報;2015年18期

4 羅賢縉;岳黎明;甄成剛;;風電場數(shù)據(jù)中心Hadoop云平臺作業(yè)調(diào)度算法研究[J];計算機工程與應用;2015年15期

5 孟祥萍;周來;王暉;紀秀;;基于hadoop云平臺的智能電網(wǎng)MapReduce數(shù)據(jù)計算技術研究[J];電測與儀表;2015年10期

6 王軍輝;賈嶸;譚泊;;基于EEMD和模糊C均值聚類的風電機組齒輪箱故障診斷[J];太陽能學報;2015年02期

7 王德文;孫志偉;;電力用戶側(cè)大數(shù)據(jù)分析與并行負荷預測[J];中國電機工程學報;2015年03期

8 彭小圣;鄧迪元;程時杰;文勁宇;李朝暉;牛林;;面向智能電網(wǎng)應用的電力大數(shù)據(jù)關鍵技術[J];中國電機工程學報;2015年03期

9 宋亞奇;周國亮;朱永利;李莉;王劉旺;王德文;;云平臺下輸變電設備狀態(tài)監(jiān)測大數(shù)據(jù)存儲優(yōu)化與并行處理[J];中國電機工程學報;2015年02期

10 潘海寧;張軍;秦明;馮健;李明輝;;基于能量譜特征的變速風機振動調(diào)制信號的檢測方法[J];中國電機工程學報;2014年S1期

相關博士學位論文 前1條

1 胡愛軍;Hilbert-Huang變換在旋轉(zhuǎn)機械振動信號分析中的應用研究[D];華北電力大學（河北）;2008年

相關碩士學位論文 前2條

1 楊繼明;基于Hadoop云平臺風電機組振動數(shù)據(jù)處理的技術研究[D];華北電力大學;2015年

2 高子U，

本文編號：2017605