本文選題：風(fēng)電機(jī)組軸承 + 故障診斷��； 參考：《新疆大學(xué)》2017年碩士論文

【摘要】：隨著全球風(fēng)力發(fā)電行業(yè)快速發(fā)展,風(fēng)電機(jī)組運(yùn)維和故障診斷市場需求逐步增加,而風(fēng)電機(jī)組軸承作為風(fēng)電機(jī)組關(guān)鍵部件之一,其正常、穩(wěn)定運(yùn)行直接影響著風(fēng)電機(jī)組能量轉(zhuǎn)化率和機(jī)組其他部件健康狀態(tài)。振動(dòng)監(jiān)測是目前軸承狀態(tài)監(jiān)測故障診斷的常用方法,風(fēng)電機(jī)組數(shù)量多、振動(dòng)測點(diǎn)多、采樣率高造成數(shù)據(jù)量非常大,達(dá)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(jì)算分析和處理新方法、新技術(shù)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(yàn)?zāi)B(tài)分解與峭度系數(shù)和相關(guān)系數(shù)的關(guān)聯(lián)度提取方法,通過時(shí)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(jī),訓(xùn)練故障嚴(yán)重程度的診斷模型,通過實(shí)驗(yàn)室軸承數(shù)據(jù)和風(fēng)電機(jī)組實(shí)際運(yùn)行軸承數(shù)據(jù),驗(yàn)證了模型故障識(shí)別的準(zhǔn)確率。通過對部分?jǐn)?shù)據(jù)探索和研究,提出了風(fēng)電機(jī)組軸承故障診斷云端化,運(yùn)用亞馬遜提供的AWS云計(jì)算平臺(tái),搭建基于AWS的風(fēng)電機(jī)組軸承故障診斷研究平臺(tái),將采用的分析方法向云端進(jìn)行算法的并行化和遷移,主要通過Python開發(fā)語言實(shí)現(xiàn)了多風(fēng)電場多臺(tái)機(jī)組振動(dòng)信號(hào)實(shí)時(shí)信號(hào)采集、傳輸和處理,同時(shí),針對每臺(tái)風(fēng)電機(jī)組軸承振動(dòng)信號(hào)歷史數(shù)據(jù)定期進(jìn)行批處理,將其故障診斷和識(shí)別模型迭代和更新,實(shí)現(xiàn)了風(fēng)機(jī)主軸承故障診斷專業(yè)化和定制化,驗(yàn)證和實(shí)現(xiàn)了風(fēng)電機(jī)組故障診斷與云計(jì)算技術(shù)結(jié)合,對風(fēng)電機(jī)組運(yùn)維和故障診斷等領(lǐng)域具有較強(qiáng)的指導(dǎo)意義和參考價(jià)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(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

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本文編號(hào)：2017605