發(fā)布時間：2018-01-26 13:01

  本文關(guān)鍵詞： 風力發(fā)電機組 傳動系統(tǒng) 狀態(tài)監(jiān)測 故障診斷 專家系統(tǒng)　出處：《華北電力大學》2014年碩士論文　論文類型：學位論文

【摘要】：風力發(fā)電機組傳動系統(tǒng)自身結(jié)構(gòu)復雜，在故障檢測和維修中都會占用很長時間，對電力生產(chǎn)造成的影響也很大，因此對風力發(fā)電機組傳動系統(tǒng)的故障進行預警和診斷具有重要的實際價值。論文依據(jù)信號分析和人工智能技術(shù)，，對風力發(fā)電機組傳動系統(tǒng)振動監(jiān)測與故障診斷系統(tǒng)展開研究。 論文首先以某風場某型號1.5兆瓦風力發(fā)電機組齒輪箱和軸承為研究對象，計算了齒輪箱各級齒輪和軸承的故障特征頻率，分析了齒輪箱高速軸測點的振動信號，提取了3種故障信號的特征，特征分析結(jié)果與開箱檢查的故障相符合，為專家系統(tǒng)的故障特征知識庫的建立奠定了基礎。 然后論文構(gòu)建了專家系統(tǒng)的知識庫和推理機，結(jié)合傳動系統(tǒng)的信號處理，分析了正常和故障零件的峭度指標值、頻譜能量分布值、信息熵值的差異。確定了峭度指標作為監(jiān)測特征參數(shù)，以頻譜能量分布值與信息熵作為故障特征參數(shù)。以訓練后BP神經(jīng)網(wǎng)絡作為專家系統(tǒng)故障診斷的推理機，將提取到的齒輪箱振動信號的故障特征參數(shù)作為輸入樣本進行仿真測試，測試結(jié)果顯示推理機能夠?qū)崿F(xiàn)故障診斷的目標。 最后，基于LabVIEW軟件的開發(fā)環(huán)境，設計了風力發(fā)電機組傳動系統(tǒng)振動監(jiān)測與故障診斷系統(tǒng)，系統(tǒng)能實現(xiàn)的功能主要包括信號的時域波形顯示、頻域分析、降噪處理、故障預警、故障診斷、數(shù)據(jù)管理等。
[Abstract]:The structure of the wind turbine transmission system is complex, which will take a long time in the fault detection and maintenance, and also has a great impact on the power production. Therefore, the early warning and diagnosis of wind turbine transmission system has important practical value. The paper is based on signal analysis and artificial intelligence technology. The vibration monitoring and fault diagnosis system of wind turbine transmission system is studied. Firstly, the gearbox and bearing of a 1.5-megawatt wind turbine in a certain wind field are studied, and the fault characteristic frequency of gear box and bearing is calculated. The vibration signals of gearbox high speed shaft measuring points are analyzed, and the characteristics of three kinds of fault signals are extracted. The results of characteristic analysis are consistent with those of open box inspection. It lays a foundation for the establishment of fault feature knowledge base of expert system. Then the knowledge base and inference machine of expert system are constructed, and the kurtosis index value and spectrum energy distribution value of normal and fault parts are analyzed in combination with the signal processing of transmission system. The kurtosis index is determined as the monitoring characteristic parameter, the spectrum energy distribution value and the information entropy as the fault characteristic parameter, and the trained BP neural network as the inference machine of expert system fault diagnosis. The fault characteristic parameters of the vibration signal of the gearbox are used as input samples for simulation test. The test results show that the inference machine can achieve the goal of fault diagnosis. Finally, based on the development environment of LabVIEW software, the vibration monitoring and fault diagnosis system of wind turbine transmission system is designed. The main functions of the system include signal display in time domain. Frequency domain analysis, noise reduction, fault warning, fault diagnosis, data management, etc.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP277;TM315

【參考文獻】

相關(guān)期刊論文 前10條

1 宋曉萍;廖明夫;;基于Internet的風電場SCADA系統(tǒng)框架設計[J];電力系統(tǒng)自動化;2006年17期

2 林小進;楊善水;王莉;龔春英;;非并網(wǎng)風電SCADA系統(tǒng)設計[J];電力系統(tǒng)自動化;2008年21期

3 孫振明,徐敏強,王日新;齒輪箱振動信號降噪方法的研究[J];風機技術(shù);2002年01期

4 汪光陽;周義蓮;;風機振動故障診斷綜述[J];安徽工業(yè)大學學報(自然科學版);2006年01期

5 陳長征;周洋;;基于MSP430的風力發(fā)電機振動監(jiān)測系統(tǒng)[J];信息技術(shù);2010年03期

6 劉長虹,陳虬;基于信息熵理論中的含模糊參數(shù)的響應面法[J];機械強度;2003年02期

7 申_",黃樹紅,韓守木,楊叔子;旋轉(zhuǎn)機械振動信號的信息熵特征[J];機械工程學報;2001年06期

8 譚繼勇;陳雪峰;何正嘉;;沖擊信號的隨機共振自適應檢測方法[J];機械工程學報;2010年23期

9 陳雪峰;李繼猛;程航;李兵;何正嘉;;風力發(fā)電機狀態(tài)監(jiān)測和故障診斷技術(shù)的研究與進展[J];機械工程學報;2011年09期

10 靳曉雄;減速箱軸承故障診斷特征參數(shù)靈敏性比較[J];建筑機械化;1994年06期

本文編號：1465682