本文選題：大型風(fēng)電機(jī)組齒輪箱 + 非線性動(dòng)力學(xué)��； 參考：《機(jī)械科學(xué)研究總院》2016年博士論文

【摘要】：近年來我國風(fēng)電行業(yè)發(fā)展迅速,裝機(jī)容量逐年遞增。大型風(fēng)機(jī)長期在野外工作,工況惡劣,很多早期機(jī)械故障很難被及時(shí)發(fā)現(xiàn)和治理,長時(shí)間運(yùn)行演變?yōu)閲?yán)重故障,甚至導(dǎo)致重大事故,嚴(yán)重影響風(fēng)電企業(yè)的經(jīng)濟(jì)效益。在大型風(fēng)機(jī)的多個(gè)關(guān)鍵部件中,齒輪箱是故障多發(fā)部件,其出現(xiàn)嚴(yán)重故障時(shí),維修困難且維修成本極高,因此對(duì)大型風(fēng)機(jī)齒輪箱進(jìn)行早期故障診斷研究,以期及早發(fā)現(xiàn)齒輪箱的潛在故障,進(jìn)行預(yù)知維護(hù)維修,對(duì)企業(yè)降低運(yùn)行維護(hù)成本,提高經(jīng)濟(jì)效益具有重要意義。以風(fēng)電場的主流機(jī)型即雙饋式變槳變速機(jī)型的增速齒輪箱為主要研究對(duì)象,運(yùn)用潤滑油液金屬磨粒在線檢測與振動(dòng)信號(hào)分析相結(jié)合的方法對(duì)齒輪箱早期故障診斷開展研究,首先通過在線檢測潤滑油液中的金屬磨粒信息判斷齒輪箱磨損程度及潤滑油受污染程度,實(shí)現(xiàn)定性預(yù)判齒輪箱的早期故障,進(jìn)而采用振動(dòng)信號(hào)的分析方法深入分析故障原因及部位,實(shí)現(xiàn)齒輪箱的早期故障診斷。論文主要涉及如下內(nèi)容：(1)大型風(fēng)電機(jī)組齒輪箱非線性動(dòng)力學(xué)研究�；诜蔷�性動(dòng)力學(xué)理論,在考慮時(shí)變嚙合剛度條件下建立了行星齒輪傳動(dòng)的非線性動(dòng)力學(xué)模型,得到了不同轉(zhuǎn)速及負(fù)載工況下齒輪嚙合和軸承支撐的正常、故障等條件下系統(tǒng)各個(gè)部件的時(shí)間曲線、頻率、相圖等。結(jié)果表明：齒輪嚙合和軸承支撐正常、齒輪嚙合故障和軸承支撐故障等條件下,輸入軸轉(zhuǎn)頻對(duì)系統(tǒng)固有特征信號(hào)具有調(diào)制影響,導(dǎo)致系統(tǒng)響應(yīng)頻譜中各階主頻出現(xiàn)邊頻現(xiàn)象；齒輪嚙合故障條件下,嚙合頻率2倍頻或4倍頻占主要能量；軸承支撐故障條件下系統(tǒng)支持剛度導(dǎo)致的頻率出現(xiàn)左移現(xiàn)象,同時(shí)被轉(zhuǎn)頻調(diào)制現(xiàn)象明顯。研究結(jié)果能夠?yàn)殚_展信號(hào)特征提取提供分析數(shù)據(jù)和提供部分故障現(xiàn)象評(píng)價(jià)依據(jù)。(2)基于自相關(guān)系數(shù)譜閾值信號(hào)消噪方法及改進(jìn)二階自適應(yīng)NLMS信號(hào)消噪方法的研究。大型風(fēng)機(jī)由于工況惡劣,采集的振動(dòng)信號(hào)中包含復(fù)雜的干擾噪聲以及塔筒隨機(jī)振動(dòng)的低頻噪聲。為了最大程度的消除兩種噪聲成分,提出了基于自相關(guān)系數(shù)譜閾值信號(hào)的消噪方法用于消除隨機(jī)干擾噪聲,并以該方法為基礎(chǔ),進(jìn)一步提出了分組自相關(guān)閾值去噪方法及閾值自動(dòng)獲取方法；提出了改進(jìn)二階自適應(yīng)NLMS消噪方法用于消除齒輪箱振動(dòng)信號(hào)中耦合的塔筒隨機(jī)低頻振動(dòng)噪聲成分。開展了仿真信號(hào)及實(shí)測信號(hào)驗(yàn)證分析,結(jié)果表明：該兩種方法對(duì)于消除振動(dòng)信號(hào)中的隨機(jī)干擾噪聲及耦合的塔筒隨機(jī)低頻噪聲具有較好的預(yù)處理效果。(3)基于階次重采樣的希爾伯特變換解調(diào)自相關(guān)功率譜特征提取方法研究。首先,進(jìn)行了轉(zhuǎn)軸角度三次方程擬合的等角度重采樣研究,分別通過仿真信號(hào)和實(shí)測信號(hào)驗(yàn)證了三次方程擬合法的有效性。然后,進(jìn)行了等角度重采樣信號(hào)的希爾伯特變換解調(diào)方法研究。最后運(yùn)用理論仿真數(shù)據(jù)和試驗(yàn)數(shù)據(jù),進(jìn)行了角度重采樣信號(hào)的平方計(jì)算解調(diào)、能量計(jì)算解調(diào)以及希爾伯特變換解調(diào)方法對(duì)比分析。結(jié)果表明：計(jì)算階次重采樣信號(hào)的希爾伯特變換解調(diào)后的自相關(guān)功率譜特征提取效果較其他兩種方法有效,能夠更為準(zhǔn)確的進(jìn)行信號(hào)特征提取。(4)基于波包閾值熵t-SNE流形學(xué)習(xí)故障分離方法研究。研究了基于小波包分解時(shí)域及頻域的t-SNE故障辨識(shí)方法,通過實(shí)測數(shù)據(jù)驗(yàn)證了采用t-SNE降維處理后的流形結(jié)構(gòu)清晰,特點(diǎn)突出,能夠更好的用于辨識(shí)設(shè)備的故障狀態(tài)。實(shí)驗(yàn)數(shù)據(jù)分析表明：該方法相比其他高維數(shù)據(jù)構(gòu)造方法及流形學(xué)習(xí)方法具有更好的故障分離效果。(5)基于油液全液流在線磨粒檢測的早期齒輪箱故障診斷方法研究。重點(diǎn)設(shè)計(jì)并研發(fā)了相應(yīng)的磨粒檢測傳感器及檢測儀器系統(tǒng),提出了基于局部最大最小值的金屬磨粒識(shí)別方法,進(jìn)行了潤滑油磨粒檢測實(shí)驗(yàn)研究。結(jié)果表明：設(shè)計(jì)的金屬磨粒檢測系統(tǒng)能夠檢測到最小150微米的金屬磨粒,達(dá)到了非常好的測量效果,儀器系統(tǒng)可用于大型風(fēng)機(jī)齒輪箱早期不明顯故障的預(yù)判,通過在線檢測潤滑油液中金屬磨粒的尺寸、數(shù)量等信息及早發(fā)現(xiàn)齒輪箱的潛在故障。(6)遠(yuǎn)程風(fēng)電機(jī)組傳動(dòng)系統(tǒng)早期狀態(tài)監(jiān)測診斷系統(tǒng)開發(fā)。為實(shí)現(xiàn)大型風(fēng)機(jī)齒輪箱的遠(yuǎn)程早期故障診斷,設(shè)計(jì)了基于以太網(wǎng)的嵌入式數(shù)據(jù)采集系統(tǒng),制定了基于TCP/IP的遠(yuǎn)程數(shù)據(jù)傳輸協(xié)議,基于微軟的.net開發(fā)了B/S(瀏覽器/服務(wù)器)模式的遠(yuǎn)程監(jiān)測及早期故障診斷系統(tǒng)軟件。
[Abstract]:In recent years, the wind power industry in China has developed rapidly and the installed capacity is increasing year by year. Large fans are working in the field for a long time, and the working conditions are bad. Many early mechanical faults are difficult to be found and treated in time. Long time operation has evolved into serious faults, even leading to major accidents, which seriously affect the economic benefits of wind power enterprises. In the parts, the gear box is a fault multiple component. When it has serious failure, it is difficult to maintain and the maintenance cost is very high. Therefore, it is of great significance to study the early fault diagnosis of the large fan gear box, so as to predict the potential malfunction of the early present gear box and carry out the maintenance and maintenance. It is of great significance for the enterprise to reduce the cost of operation and maintenance and to improve the economic benefit. The main research object is the speed increasing gear box of the main type of the wind electric field, that is the double fed variable speed variable speed model. The study of the early fault diagnosis of the gear box is carried out by the method of on-line detection and vibration signal analysis of lubricating oil metal abrasive particles. First, the gear box is judged by the on-line detection of the metal abrasive information in the lubricating oil. The degree of wear and the degree of contamination of the lubricating oil can be used to determine the early fault of the gearbox, and then the analysis method of vibration signal is used to analyze the causes and parts of the fault. The main contents of this paper are as follows: (1) the nonlinear dynamics of the gearbox of the large wind turbine group. The nonlinear dynamic model of the planetary gear transmission is established under the condition of time-varying meshing stiffness. The time curve, frequency and phase diagram of the parts of the system under the conditions of gear meshing and bearing support under different speeds and load conditions are obtained. The results show that the gear meshing and bearing support are normal, Under the conditions of gear meshing failure and bearing support fault, the input shaft frequency has modulation influence on the inherent characteristic signal of the system, which leads to the occurrence of the main frequency in the response spectrum of the system; the meshing frequency is 2 frequency doubling or 4 frequency doubling of the main energy under the gear meshing fault condition, and the system support stiffness is caused by the bearing support failure. The results can provide analysis data for signal feature extraction and provide some basis for evaluation of fault phenomena. (2) study on the method of denoising based on the threshold signal of autocorrelation coefficient spectrum threshold signal and the improvement of the two order adaptive NLMS signal denoising method. In order to eliminate two kinds of noise components, a denoising method based on the autocorrelation coefficient spectrum threshold signal is proposed to eliminate the random interference noise, and based on this method, the packet autocorrelation threshold is further proposed. An improved two order adaptive NLMS denoising method is proposed to eliminate the random low-frequency vibration noise components of the coupling of the gear box vibration signals. The simulation and measured signal verification and analysis are carried out. The results show that the two methods are used to eliminate the random interference noise in the vibration signals and the results. The coupled tower barrel random low frequency noise has good preprocessing effect. (3) study on the autocorrelation power spectrum feature extraction method of Hilbert transform demodulation based on order resampling. First, the equal angle resampling study of the three times equation fitting of the rotating axis angle is carried out, and the three equation fitting is verified by the imitation real signal and the measured signal respectively. Then, the Hilbert transform demodulation method of the equal angle resampling signal is studied. Finally, the theoretical simulation data and the experimental data are used to carry out the square calculation and demodulation of the angle resampling signal, the energy calculation and demodulation and the Hilbert transform demodulation method. The results show that the order resampling is calculated. The autocorrelation power spectrum feature extraction effect after the signal Hilbert transform demodulation is more effective than the other two methods. (4) study on the fault separation method based on the packet threshold entropy t-SNE manifold learning. The t-SNE fault identification method based on the time domain and frequency domain of wavelet packet decomposition is studied. The data verify that the manifold structure with t-SNE dimension reduction is clear and characteristic, and it can be used to identify the fault status of the equipment better. The experimental data analysis shows that the method has better fault separation effect compared with other high dimensional data construction methods and manifold learning methods. (5) based on the oil liquid full liquid flow on-line abrasive detection The fault diagnosis method of early gear box is studied. The corresponding abrasive detection sensor and detecting instrument system are designed and developed. The metal abrasive recognition method based on the local maximum and minimum value is put forward, and the experimental research of lubricating oil abrasive particle detection is carried out. The result shows that the design of the gold particle detection system can detect the minimum of 150 micro. The metal abrasive grain of rice has achieved very good measurement effect. The instrument system can be used to predict the early non obvious fault of the large fan gear box. By on-line measuring the size and quantity of metal abrasive in the lubricating oil, the information of the quantity and the potential fault of the early present gear box. (6) the early state monitoring and diagnosis system of the transmission system of the long distance wind turbine is opened. In order to realize the remote early fault diagnosis of large fan gear box, an embedded data acquisition system based on Ethernet is designed, and a remote data transmission protocol based on TCP/IP is developed. Based on the.Net of Microsoft, the remote monitoring and early fault diagnosis system software of B/S (Browser / server) mode is developed.

【學(xué)位授予單位】：機(jī)械科學(xué)研究總院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM315;TH132.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 向丹;岑健;;基于EMD熵特征融合的滾動(dòng)軸承故障診斷方法[J];航空動(dòng)力學(xué)報(bào);2015年05期

2 王志娟;趙軍紅;丁桂甫;;新型三線圈式滑油磨粒在線監(jiān)測傳感器[J];納米技術(shù)與精密工程;2015年02期

3 王志堅(jiān);韓振南;劉邱祖;寧少慧;;基于MED-EEMD的滾動(dòng)軸承微弱故障特征提取[J];農(nóng)業(yè)工程學(xué)報(bào);2014年23期

4 陳學(xué)峰;李加唐;;在線油液監(jiān)測技術(shù)及其應(yīng)用[J];機(jī)械工程與自動(dòng)化;2014年06期

5 毛向德;王慶賢;董唯光;梁金平;朱科;;小波包神經(jīng)網(wǎng)絡(luò)與數(shù)據(jù)降維的移相全橋變換器的故障診斷[J];電源學(xué)報(bào);2014年04期

6 谷玉海;韓秋實(shí);徐小力;;高精度轉(zhuǎn)速測量方法研究[J];儀表技術(shù)與傳感器;2013年10期

7 張志春;劉洪偉;;風(fēng)電機(jī)組狀態(tài)監(jiān)測技術(shù)及故障分析方法[J];標(biāo)準(zhǔn)科學(xué);2013年06期

8 萬鵬;王紅軍;徐小力;;局部切空間排列和支持向量機(jī)的故障診斷模型[J];儀器儀表學(xué)報(bào);2012年12期

9 鄒今春;沈玉娣;;變工況齒輪箱故障診斷方法綜述[J];機(jī)械傳動(dòng);2012年08期

10 徐小力;蔣章雷;任彬;陳濤;;基于Birgé-Massart閾值的煙氣發(fā)電機(jī)組狀態(tài)特征弱信息提取方法[J];機(jī)械工程學(xué)報(bào);2012年12期

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