發(fā)布時(shí)間：2018-11-03 11:23

【摘要】：我國(guó)作為一個(gè)能源需求大國(guó),對(duì)能源的需求與日俱增。但我國(guó)的能源結(jié)構(gòu)處于欠合理狀態(tài),主要表現(xiàn)為對(duì)于化石能源的依賴嚴(yán)重,清潔能源占比不高等問題。隨著化石能源的枯竭、環(huán)境惡化等問題的出現(xiàn),都要求我國(guó)逐步發(fā)展清潔能源來改變傳統(tǒng)的能源結(jié)構(gòu)。在眾多清潔能源中,風(fēng)能作為其中最具代表性的一種,憑借著分布廣泛、商業(yè)化程度高、技術(shù)成熟等優(yōu)勢(shì)正在越來越多的發(fā)揮著重要作用。但風(fēng)電機(jī)組工作環(huán)境惡劣,經(jīng)常面臨風(fēng)速不穩(wěn)定、內(nèi)外環(huán)境溫差大、載荷多變等問題。不同類型的風(fēng)電機(jī)組也將面臨不同的惡劣環(huán)境,如海上風(fēng)電機(jī)組所處環(huán)境空氣濕度大、鹽分高,機(jī)組中零部件易受到腐蝕;陸上風(fēng)電機(jī)組面臨的最大環(huán)境問題是空氣中沙塵大,當(dāng)機(jī)組密封條件不佳時(shí),沙塵進(jìn)入機(jī)組極易造成齒輪損壞等問題,眾多因素導(dǎo)致了風(fēng)電場(chǎng)運(yùn)維成本持續(xù)居高不下。據(jù)估計(jì),在風(fēng)電機(jī)組的運(yùn)行壽命周期內(nèi),運(yùn)維費(fèi)用是發(fā)電總成本的重要組成部分,約占總成本的25%~30%。同時(shí),對(duì)海上風(fēng)電機(jī)組的運(yùn)行統(tǒng)計(jì)中發(fā)現(xiàn),50%的停運(yùn)時(shí)間是由齒輪箱故障造成的。根據(jù)以上數(shù)據(jù)可以看出,對(duì)風(fēng)電機(jī)組行星齒輪箱運(yùn)行狀態(tài)做出及時(shí)的識(shí)別與診斷,具有極大實(shí)際應(yīng)用意義。本文主要研究了風(fēng)電機(jī)組行星齒輪箱故障的主要原因及其故障檢測(cè)的有效方法:1)分析了風(fēng)電機(jī)組行星齒輪箱中不同類型故障出現(xiàn)的主要原因,總結(jié)了不同類型的故障特征,并針對(duì)不同故障特征提出了對(duì)應(yīng)的運(yùn)行維護(hù)方法,提高了機(jī)組運(yùn)行的可靠性。2)提出一種基于改進(jìn)EEMD的自適應(yīng)信號(hào)分解方法。可以針對(duì)不同信號(hào)自適應(yīng)給出不同的分解參數(shù),在實(shí)際應(yīng)用中一定程度的解決了傳統(tǒng)EEMD分解過程中的模態(tài)混疊問題、提高了計(jì)算速度、改善了分解效果。達(dá)到了信號(hào)自適應(yīng)分解的目的。3)利用提出的改進(jìn)EEMD方法實(shí)際信號(hào)進(jìn)行分解,然后使用單重分形維數(shù)提取經(jīng)改進(jìn)EEMD分解后各個(gè)分量中的分形特征,通過對(duì)比信號(hào)特征實(shí)現(xiàn)了對(duì)行星齒輪箱故障的實(shí)時(shí)有效的診斷。4)利用多重分形維數(shù)譜與支持向量機(jī)的結(jié)合方法,實(shí)現(xiàn)了對(duì)行星齒輪箱在不同轉(zhuǎn)速情況下的故障的診斷,進(jìn)一步證實(shí)了分形維數(shù)對(duì)于信號(hào)特征具有良好的提取能力。同時(shí)證明了支持向量機(jī)對(duì)于信號(hào)的分類效果良好。5)最后通過使用單重分形維數(shù)提取經(jīng)過改進(jìn)EEMD分解后的各個(gè)分量的分形特性,然后將提取的得到的信號(hào)特征作為支持向量機(jī)的輸入向量進(jìn)行信號(hào)分類,實(shí)現(xiàn)了對(duì)實(shí)際故障信號(hào)的有效診斷。
[Abstract]:As a large country of energy demand, China has a growing demand for energy. However, the energy structure of our country is in an unreasonable state, which is mainly due to the heavy dependence on fossil energy and the low proportion of clean energy. With the depletion of fossil energy and the appearance of environmental deterioration, China needs to develop clean energy gradually to change the traditional energy structure. Among the many clean energy sources, wind energy, as one of the most representative, is playing an important role with the advantages of wide distribution, high degree of commercialization, mature technology and so on. However, wind turbine often faces some problems such as unstable wind speed, large temperature difference between inside and outside environment, variable load and so on. Different types of wind turbine units will also be faced with different adverse environment, such as offshore wind turbine units in high air humidity, high salinity, unit components are vulnerable to corrosion; The biggest environmental problem faced by onshore wind turbines is the large dust in the air. When the sealing conditions of the units are not good, the sand dust entering the units is easy to cause gear damage and so on. Many factors cause the operation and maintenance costs of the wind farms to remain high. It is estimated that the cost of operation and maintenance is an important part of the total cost of power generation in the operational life cycle of wind turbine, accounting for about 2530% of the total cost. At the same time, it is found that 50% of the outage time is caused by gearbox failure. It can be seen from the above data that it is of great practical significance to identify and diagnose the running state of planetary gearbox of wind turbine unit in time. In this paper, the main causes of planetary gearbox faults of wind turbine and the effective methods of fault detection are studied. 1) the main causes of different types of faults in planetary gearboxes of wind turbines are analyzed, and the characteristics of different types of faults are summarized. According to different fault characteristics, the corresponding operation and maintenance methods are proposed to improve the reliability of unit operation. 2) an adaptive signal decomposition method based on improved EEMD is proposed. Different decomposition parameters can be given according to different signal adaptations. In practical application, modal aliasing in the traditional EEMD decomposition process is solved to a certain extent, and the calculation speed is improved and the decomposition effect is improved. The purpose of adaptive signal decomposition is achieved. 3) the improved EEMD method is used to decompose the actual signal, and then the fractal features of each component after the improved EEMD decomposition are extracted by using the single multifractal dimension. The real-time and effective fault diagnosis of planetary gearbox is realized by comparing the signal features. 4) the fault diagnosis of planetary gearbox under different rotational speeds is realized by the combination of multifractal dimension spectrum and support vector machine. It is further proved that fractal dimension has a good ability to extract signal features. At the same time, it is proved that SVM has a good effect on signal classification. 5) finally, the fractal characteristics of each component after improved EEMD decomposition are extracted by using single multifractal dimension. Then, the extracted signal features are classified as input vectors of support vector machine (SVM), and the effective diagnosis of actual fault signals is realized.
【學(xué)位授予單位】：上海電力學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM315

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 宋平崗;章偉;林家通;游小輝;羅劍;;基于數(shù)學(xué)形態(tài)學(xué)廣義分形維數(shù)逆變器故障診斷[J];華東交通大學(xué)學(xué)報(bào);2016年06期

2 王志強(qiáng);宮虎;房豐洲;劉冰;;長(zhǎng)度分形維數(shù)在微銑刀磨損狀態(tài)識(shí)別中的應(yīng)用[J];振動(dòng).測(cè)試與診斷;2016年03期

3 劉昱;張俊紅;畢鳳榮;林杰威;馬文朋;馬梁;;基于Wigner分布和分形維數(shù)的柴油機(jī)故障診斷[J];振動(dòng).測(cè)試與診斷;2016年02期

4 黃玲玲;曹家麟;張開華;符楊;徐涵璐;;海上風(fēng)電機(jī)組運(yùn)行維護(hù)現(xiàn)狀研究與展望[J];中國(guó)電機(jī)工程學(xué)報(bào);2016年03期

5 楊凱;張認(rèn)成;楊建紅;杜建華;陳首虹;涂然;;基于分形維數(shù)和支持向量機(jī)的串聯(lián)電弧故障診斷方法[J];電工技術(shù)學(xué)報(bào);2016年02期

6 孫曙光;龐毅;王景芹;張超;杜太行;于晗;;一種基于新型小波閾值去噪預(yù)處理的EEMD諧波檢測(cè)方法[J];電力系統(tǒng)保護(hù)與控制;2016年02期

7 林近山;竇春紅;張妮;;基于多重分形去趨勢(shì)互相關(guān)分析的齒輪箱故障診斷[J];機(jī)械傳動(dòng);2016年01期

8 姜紹飛;陳志剛;沈清華;吳銘昊;麻勝蘭;;基于EEMD與FastICA的損傷異常識(shí)別與定位[J];振動(dòng)與沖擊;2016年01期

9 孔德同;劉慶超;雷亞國(guó);范煒;丁小川;王志;;一種改進(jìn)的EEMD方法及其應(yīng)用研究[J];振動(dòng)工程學(xué)報(bào);2015年06期

10 岳曉峰;邵海賀;;基于改進(jìn)EEMD的滾動(dòng)軸承故障診斷研究[J];制造業(yè)自動(dòng)化;2015年17期

相關(guān)博士學(xué)位論文 前2條

1 張永平;分形的控制與應(yīng)用[D];山東大學(xué);2008年

2 許高峰;全封閉組合電器局部放電信號(hào)內(nèi)置傳感檢測(cè)和分形特征提取的研究[D];重慶大學(xué);2003年

相關(guān)碩士學(xué)位論文 前1條

1 衛(wèi)一多;風(fēng)電增速行星齒輪傳動(dòng)系統(tǒng)的振動(dòng)分析[D];西安理工大學(xué);2009年

，

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