本文選題：大型風電機組齒輪箱 + 非線性動力學　； 參考：《機械科學研究總院》2016年博士論文

【摘要】：近年來我國風電行業(yè)發(fā)展迅速,裝機容量逐年遞增。大型風機長期在野外工作,工況惡劣,很多早期機械故障很難被及時發(fā)現和治理,長時間運行演變?yōu)閲乐毓收?甚至導致重大事故,嚴重影響風電企業(yè)的經濟效益。在大型風機的多個關鍵部件中,齒輪箱是故障多發(fā)部件,其出現嚴重故障時,維修困難且維修成本極高,因此對大型風機齒輪箱進行早期故障診斷研究,以期及早發(fā)現齒輪箱的潛在故障,進行預知維護維修,對企業(yè)降低運行維護成本,提高經濟效益具有重要意義。以風電場的主流機型即雙饋式變槳變速機型的增速齒輪箱為主要研究對象,運用潤滑油液金屬磨粒在線檢測與振動信號分析相結合的方法對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,實現定性預判齒輪箱的早期故障,進而采用振動信號的分析方法深入分析故障原因及部位,實現齒輪箱的早期故障診斷。論文主要涉及如下內容：(1)大型風電機組齒輪箱非線性動力學研究�；诜蔷�性動力學理論,在考慮時變嚙合剛度條件下建立了行星齒輪傳動的非線性動力學模型,得到了不同轉速及負載工況下齒輪嚙合和軸承支撐的正常、故障等條件下系統各個部件的時間曲線、頻率、相圖等。結果表明：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,輸入軸轉頻對系統固有特征信號具有調制影響,導致系統響應頻譜中各階主頻出現邊頻現象；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支持剛度導致的頻率出現左移現象,同時被轉頻調制現象明顯。研究結果能夠為開展信號特征提取提供分析數據和提供部分故障現象評價依據。(2)基于自相關系數譜閾值信號消噪方法及改進二階自適應NLMS信號消噪方法的研究。大型風機由于工況惡劣,采集的振動信號中包含復雜的干擾噪聲以及塔筒隨機振動的低頻噪聲。為了最大程度的消除兩種噪聲成分,提出了基于自相關系數譜閾值信號的消噪方法用于消除隨機干擾噪聲,并以該方法為基礎,進一步提出了分組自相關閾值去噪方法及閾值自動獲取方法；提出了改進二階自適應NLMS消噪方法用于消除齒輪箱振動信號中耦合的塔筒隨機低頻振動噪聲成分。開展了仿真信號及實測信號驗證分析,結果表明：該兩種方法對于消除振動信號中的隨機干擾噪聲及耦合的塔筒隨機低頻噪聲具有較好的預處理效果。(3)基于階次重采樣的希爾伯特變換解調自相關功率譜特征提取方法研究。首先,進行了轉軸角度三次方程擬合的等角度重采樣研究,分別通過仿真信號和實測信號驗證了三次方程擬合法的有效性。然后,進行了等角度重采樣信號的希爾伯特變換解調方法研究。最后運用理論仿真數據和試驗數據,進行了角度重采樣信號的平方計算解調、能量計算解調以及希爾伯特變換解調方法對比分析。結果表明：計算階次重采樣信號的希爾伯特變換解調后的自相關功率譜特征提取效果較其他兩種方法有效,能夠更為準確的進行信號特征提取。(4)基于波包閾值熵t-SNE流形學習故障分離方法研究。研究了基于小波包分解時域及頻域的t-SNE故障辨識方法,通過實測數據驗證了采用t-SNE降維處理后的流形結構清晰,特點突出,能夠更好的用于辨識設備的故障狀態(tài)。實驗數據分析表明：該方法相比其他高維數據構造方法及流形學習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。重點設計并研發(fā)了相應的磨粒檢測傳感器及檢測儀器系統,提出了基于局部最大最小值的金屬磨粒識別方法,進行了潤滑油磨粒檢測實驗研究。結果表明：設計的金屬磨粒檢測系統能夠檢測到最小150微米的金屬磨粒,達到了非常好的測量效果,儀器系統可用于大型風機齒輪箱早期不明顯故障的預判,通過在線檢測潤滑油液中金屬磨粒的尺寸、數量等信息及早發(fā)現齒輪箱的潛在故障。(6)遠程風電機組傳動系統早期狀態(tài)監(jiān)測診斷系統開發(fā)。為實現大型風機齒輪箱的遠程早期故障診斷,設計了基于以太網的嵌入式數據采集系統,制定了基于TCP/IP的遠程數據傳輸協議,基于微軟的.net開發(fā)了B/S(瀏覽器/服務器)模式的遠程監(jiān)測及早期故障診斷系統軟件。
[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.

【學位授予單位】：機械科學研究總院
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TM315;TH132.41

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