本文選題：風(fēng)力發(fā)電機(jī)組　切入點：傳動鏈　出處：《華北電力大學(xué)》2013年博士論文　論文類型：學(xué)位論文

【摘要】：振動監(jiān)測是機(jī)械設(shè)備狀態(tài)監(jiān)測與故障診斷的主要技術(shù)之一,在許多工業(yè)行業(yè)得到廣泛應(yīng)用。我國早期建設(shè)的風(fēng)電場出于降低成本的考慮,一般不配備風(fēng)電機(jī)組振動監(jiān)測系統(tǒng)。隨著設(shè)備運行時間的不斷延續(xù),風(fēng)電機(jī)組傳動鏈故障率及維修費用高的問題逐漸突出,風(fēng)電企業(yè)開始重視風(fēng)電機(jī)組的運行狀態(tài)監(jiān)測及故障診斷,特別是國家能源局于2011年頒布的推薦性國家能源行業(yè)標(biāo)準(zhǔn)NB/T31004-2011《風(fēng)力發(fā)電機(jī)組振動狀態(tài)監(jiān)測導(dǎo)則》,對于風(fēng)電機(jī)組振動監(jiān)測與故障診斷技術(shù)的發(fā)展起到了有力的推動作用。 風(fēng)電機(jī)組傳動鏈主要由傳動軸及支撐軸承、增速齒輪箱等旋轉(zhuǎn)部件組成,是連接風(fēng)輪和發(fā)電機(jī),實現(xiàn)能量轉(zhuǎn)換和傳遞過程的關(guān)鍵部件。與一般機(jī)械設(shè)備中的傳動鏈相比,風(fēng)電機(jī)組傳動鏈的載荷狀態(tài)、運行工況、環(huán)境條件和結(jié)構(gòu)布局等方面均比較復(fù)雜,導(dǎo)致傳動鏈中齒輪、軸承等主要零部件的故障機(jī)理和故障發(fā)展模式等存在特殊性,故障率高于其它行業(yè)的同類設(shè)備,實際使用壽命遠(yuǎn)遠(yuǎn)低于設(shè)計壽命。因此,針對風(fēng)電傳動鏈的運行特點及其故障診斷的共性問題,研究新的理論和技術(shù)解決方法,提高故障診斷和預(yù)測準(zhǔn)確性,對于保證風(fēng)電機(jī)組設(shè)備的健康運行具有重要意義。 風(fēng)電機(jī)組傳動鏈?zhǔn)菣C(jī)、電、液耦合的復(fù)雜結(jié)構(gòu),可能產(chǎn)生故障的部位多,從故障激勵源到振動監(jiān)測點經(jīng)過不同傳遞途徑的衰減和混合作用,監(jiān)測到的振動信號往往是不同激勵源及傳遞途徑的復(fù)雜卷積混合作用,具有背景噪聲干擾大、非平穩(wěn)、非線性的特點,在許多實際場合,用經(jīng)典的振動信號分析方法難以給出反映故障特征的準(zhǔn)確信息,影響了對故障的精確分析診斷,更限制了自動故障診斷和預(yù)測技術(shù)的應(yīng)用。因此,根據(jù)風(fēng)電機(jī)組傳動鏈中齒輪箱和支撐軸承的特殊性,研究在經(jīng)典振動信號分析方法的基礎(chǔ)上的改進(jìn)方法,提高故障特征分析與提取功能和效果,是實現(xiàn)準(zhǔn)確故障診斷的技術(shù)關(guān)鍵。本文在這樣的背景下,探討了對幾種經(jīng)典振動信號分析的改進(jìn),并分別用雙饋式和直驅(qū)式風(fēng)電機(jī)組傳動鏈的實測信號進(jìn)行驗證。主要研究內(nèi)容和形成的結(jié)論如下： (1)倒頻譜具有一個“同態(tài)濾波”的重要性質(zhì),可以實現(xiàn)卷積混合信號的分離。利用倒頻譜的同態(tài)濾波性質(zhì),提出一種提取齒輪箱故障特征的方法,由于監(jiān)測到的振動信號是激勵源與傳遞過程的結(jié)構(gòu)固有振動的卷積結(jié)果,這兩種信號成分在倒頻域的特性存在明顯差別。據(jù)此,對齒輪箱振動信號在倒頻域內(nèi)進(jìn)行帶通濾波處理,將激勵源成分和結(jié)構(gòu)固有振動成分進(jìn)行分離,然后對具有低倒頻性質(zhì)的結(jié)構(gòu)共振成分進(jìn)行頻譜重構(gòu),用重構(gòu)頻譜反映故障引起的結(jié)構(gòu)固有特性的變化,從而揭示故障發(fā)生和發(fā)展的趨勢。對風(fēng)電齒輪箱和軸承振動的分析實例表明,該方法提取的特征值具有受運行工況影響小,在各種運行工況下都能夠清楚表征故障狀態(tài),而且可以很好地反映故障的發(fā)生發(fā)展趨勢。 (2)基于Hilbert變換的窄帶包絡(luò)分析通過選擇振動信號中反映結(jié)構(gòu)共振的頻帶進(jìn)行窄帶濾波,獲取反映故障的幅值包絡(luò)信息。該方法的主要問題是窄帶濾波頻帶不好確定,特別是對于風(fēng)電傳動鏈這類復(fù)雜機(jī)械設(shè)備,濾波頻帶的選擇對包絡(luò)分析結(jié)果影響很大,甚至可能得出錯誤的診斷結(jié)果。針對這一問題,提出一種稱為“移動濾波包絡(luò)譜圖”的改進(jìn)方法,利用中心頻率移動的窄帶濾波器對振動信號進(jìn)行分頻段窄帶濾波處理,求出每段濾波信號的幅值包絡(luò)譜,構(gòu)成一組隨窄帶濾波中心頻率變化的“頻-頻”域的包絡(luò)譜陣,定義為“移動濾波包絡(luò)譜圖”。實例分析表明,移動濾波包絡(luò)譜圖可以清楚地區(qū)分正常狀態(tài)和故障狀態(tài),直截了當(dāng)?shù)胤从彻收弦鸬恼駝有盘柛鱾�頻段的變化。 (3)齒輪、軸承等旋轉(zhuǎn)機(jī)械零部件故障造成振動信號中隨機(jī)成分的變化,對信號的循環(huán)平穩(wěn)特性產(chǎn)生影響,因此可以用循環(huán)平穩(wěn)特性分析中隨機(jī)成分的變化反映故障。但是振動信號中隨機(jī)成分的能量一般較低、分布頻率范圍較寬,為此提出利用譜相關(guān)密度三維對數(shù)等高圖顯示故障引起的信號隨機(jī)成分的變化進(jìn)行故障特征提取。通過試驗軸承和直驅(qū)式風(fēng)電機(jī)組軸承的實測振動信號對比分析,表明軸承故障產(chǎn)生振動信號的變化可以用對數(shù)譜相關(guān)函數(shù)圖清楚地反映出來。以譜相關(guān)函數(shù)共振區(qū)切片低頻部分(解調(diào)成分)的平均值作為特征值,由于消除了非循環(huán)平穩(wěn)成分的影響,對于故障狀態(tài)引起的變化更加敏感,有助于提高故障軸承診斷的準(zhǔn)確性。
[Abstract]:Vibration monitoring is one of the main technology of condition monitoring and fault diagnosis of machinery, is widely used in many industries. Early wind farm construction in China for cost reduction, is generally not equipped with wind turbine vibration monitoring system. With the equipment running time the continuation of the wind turbine drive train failure rate and maintenance the problem of high cost gradually prominent, wind power companies began to pay attention to the wind turbine operation condition monitoring and fault diagnosis, especially vibration monitoring guidelines > recommended national energy industry standard NB/T31004-2011< wind power generation unit of National Energy Bureau issued in 2011, for the development of wind turbine vibration monitoring and fault diagnosis technology to a strong role in promoting.
The wind turbine transmission chain is mainly composed of a drive shaft and bearing, gear box and other rotating parts is connected with a wind wheel and a generator, a key component of energy conversion and transfer process. Compared with the transmission chain of general mechanical equipment, the load state of wind turbine transmission chain operating conditions, environmental conditions the structure and layout are complicated, resulting in gear transmission chain, bearings and other major components of the failure mechanism and failure mode of development has the particularity, the failure rate is higher than that of other similar equipment industry, the actual service life is far lower than the design life. Therefore, the common problems in the operation characteristic and fault diagnosis of wind turbine drivetrain. Research, new theory and technology solutions, to improve the accuracy of fault diagnosis and prediction, it is very important to ensure the healthy operation of the wind turbine equipment.
The wind turbine transmission chain is machine, electricity, liquid coupling of complex structure, possible fault location, fault excitation from source to vibration monitoring points through different transmission attenuation and mixing, vibration monitoring signal is often complex convolution mixing different excitation sources and transfer paths, with background noise large, non-stationary and nonlinear characteristics, in many practical situations, using vibration signal analysis method is difficult to give accurate information to reflect the classical fault features and influence the accurate analysis to the fault diagnosis and application limit automatic fault diagnosis and prediction technology. Therefore, according to the particularity of the gear box and the supporting bearings of wind power the unit in the transmission chain, the improved method of basic research in the classical analysis method on vibration signal, improve fault feature analysis and extraction of function and effect, is to achieve accurate fault diagnosis technique In this context, this paper discusses the improvement of several classical vibration signals analysis, and validates the measured signals of doubly fed and direct drive wind turbines respectively. The main contents and conclusions are as follows:
(1) the important properties of the cepstrum has a "homomorphic filtering", can realize the separation of convolutive mixed signals. Homomorphic filtering properties using cepstrum, presents a method of fault feature extraction of gearbox, the vibration signal detected is the result of convolution vibration excitation source and the transfer process, there different characteristics of the two signal components in cepstrum domain. Accordingly, the gearbox vibration signal processing in bandpass filtering in cepstrum domain, the excitation source is an inherent component and structure vibration components were separated, and then the spectrum reconstruction of structure components with low frequency resonance properties, changes reflect the structural characteristics caused by fault with the reconstruction of the spectrum, so as to reveal the trend of fault occurrence and development. According to the analysis of examples of wind turbine gearbox and bearing vibration, the extracted value is by operation The effect is small, and the fault state can be clearly characterized under various operating conditions, and it can also reflect the development trend of the fault.
(2) narrow-band envelope Hilbert transform analysis with vibration signal reflects the structure resonance frequency band is narrow band filter based on gain amplitude envelope. The main fault information reflecting the problem of the method is not to determine the frequency of narrowband filter, especially for the wind turbine drivetrain this kind of complex mechanical equipment, the filtering band selection on the envelope the analysis results are very different, and may get the wrong diagnosis results. To solve this problem, we propose an improved method called "mobile filtering envelope spectrum", the vibration signal frequency narrowband filtering processing using narrowband filter with the center frequency of the mobile, calculate the amplitude envelope of each section of the filtered signal spectrum, form an envelope a group with narrowband filter center frequency changes in the frequency - domain frequency spectrum array, defined as "mobile filtering envelope spectrum. Example analysis shows that the movement of the filter envelope spectrum profile In order to clearly distinguish the normal state and the failure state of the region, the change of each frequency band of the vibration signal caused by the fault is directly reflected.
(3) gears, bearings and other parts of rotating machinery fault caused by random changes in vibration signals, the influence of the cyclostationarity of signals, it can reflect the fault with changes of circulating random component stationarity analysis. But the random components in the vibration signal energy is generally low, the distribution of a wide range of frequencies, is proposed using spectral correlation density 3D contour map shows logarithmic change signal caused by the fault of the random components for fault feature extraction. Through the test of bearing and direct drive wind turbine bearing vibration signal analysis, indicating that the change of bearing fault vibration signals can be clearly reflected by logarithmic graph spectral correlation function. Based on the spectrum the correlation function (low frequency resonance demodulation section components) the average value as the characteristic value, due to the elimination of the influence of non cyclostationary components, for fault state The resulting changes are more sensitive and help to improve the accuracy of fault bearing diagnosis.

【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TM315;TH165.3

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本文編號：1591153