面向風電機組的齒輪箱軸承故障診斷技術(shù)研究
發(fā)布時間：2018-11-16 08:09
【摘要】：齒輪箱是風電機組傳動系統(tǒng)的重要組成部分,齒輪箱的滾動軸承是傳動鏈中故障率較高的部件之一。軸承發(fā)生故障不但影響機組的正常運行,甚至波及供電側(cè)電網(wǎng)的安全平穩(wěn)運行。因此對風電機組齒輪箱軸承故障進行快速診斷具有重要的現(xiàn)實意義和使用價值。本文在深入分析風力發(fā)電機組的基本組成結(jié)構(gòu)、故障機理、故障特征及其特征頻率的基礎(chǔ)上采用經(jīng)驗模態(tài)分解(Empirical Mode Decomposition)改進閾值方法對滾動軸承出現(xiàn)故障時的振動信號進行降噪預處理,然后提取故障特征,分析故障類型。將時頻域分析技術(shù)和旋轉(zhuǎn)機械故障理論知識結(jié)合起來,針對風力發(fā)電機組的機械傳動系統(tǒng)出現(xiàn)的常見機械故障問題,如斷齒、點蝕、磨損、偏心、軸承內(nèi)圈、外圈、滾動體損壞等故障問題進行綜合分析研究,并采用智能分類算法支持向量機(Support Vector Machines)對風電機組的齒輪箱軸承進行故障分析和分類研究,采用實測實驗模擬故障數(shù)據(jù)驗證故障診斷算法的可行性和準確性,為風力發(fā)電機組故障診斷提供一種新的解決方法。研究內(nèi)容和結(jié)論如下： (1)從機械故障診斷的基本原理出發(fā)分析研究風力發(fā)電機組齒輪箱滾動軸承機械振動故障機理,分析出各個部件出現(xiàn)故障的特征頻率,明確不同部件故障所對應的故障特征,為后續(xù)故障診斷和分類實驗驗證提供數(shù)據(jù)支撐和理論依據(jù)。 (2)在獲得故障測試數(shù)據(jù)之后,采用小波分解與EMD分解閾值方法進行降噪處理、頻譜和包絡(luò)譜分析,觀察分析頻譜圖中的故障特征量,提出了改進EMD閾值降噪方法,并驗證其可行性和優(yōu)越性 (3)在故障數(shù)據(jù)中選取峰-峰值、有效值、方差和峭度值指標作為故障特征量,采用支持向量機分類識別算法對所選取的故障特征量組成的訓練樣本進行訓練,構(gòu)成故障診斷基本模型,然后采用網(wǎng)格搜索方法、遺傳算法、粒子群算法這三種參數(shù)優(yōu)化算法對支持向量機診斷模型的參數(shù)進行優(yōu)化,以獲取齒輪箱滾動軸承的故障點的精確定位和故障類型的有效辨識,仿真結(jié)果表明,網(wǎng)格搜索法雖然計算速度相對較快一些,但是故障類型分類準確率較低。遺傳算法容易陷入局部最優(yōu)并且計算速度相對較慢,分類效果欠佳。粒子群優(yōu)化算法分類準確率最高,計算速度比遺傳算法快,但是收斂性差。
[Abstract]:The gearbox is an important part of the transmission system of wind turbine. The rolling bearing of the gearbox is one of the parts with high failure rate in the transmission chain. Bearing failure not only affects the normal operation of the unit, but also affects the safe and stable operation of the power supply network. Therefore, it has important practical significance and practical value to diagnose the bearing fault of wind turbine gearbox quickly. In this paper, the basic structure and failure mechanism of wind turbine are analyzed. Based on the fault features and their characteristic frequencies, the improved threshold method of empirical mode decomposition (Empirical Mode Decomposition) is used to pre-process the vibration signals of rolling bearings in the event of failure, then the fault features are extracted and the fault types are analyzed. Combining time and frequency domain analysis technology with the knowledge of rotating machinery fault theory, this paper aims at the problems of common mechanical faults in the mechanical transmission system of wind turbine, such as tooth breaking, pitting, abrasion, eccentricity, bearing inner ring, outer ring, etc. The problems of rolling body damage and other faults are comprehensively analyzed and studied. The intelligent classification algorithm, support vector machine (Support Vector Machines), is used to analyze and classify the gearbox bearing of wind turbine. The feasibility and accuracy of the fault diagnosis algorithm are verified by the simulated fault data of the measured experiments, which provides a new method for the fault diagnosis of wind turbine generator. The research contents and conclusions are as follows: (1) based on the basic principle of mechanical fault diagnosis, the mechanism of mechanical vibration failure of roller bearing of wind turbine gearbox is analyzed, and the characteristic frequency of each component fault is analyzed. The corresponding fault characteristics of different components are defined, which provides data support and theoretical basis for subsequent fault diagnosis and classification experiment verification. (2) after obtaining the fault test data, wavelet decomposition and EMD decomposition threshold method are used to reduce the noise, the spectrum and envelope spectrum are analyzed, the fault characteristic quantity in the spectrum chart is observed and analyzed, and the improved EMD threshold denoising method is put forward. The feasibility and superiority of the method are verified. (3) the peak-peak value, effective value, variance and kurtosis are selected as the fault characteristic variables in the fault data. The support vector machine (SVM) classification and recognition algorithm is used to train the training samples composed of the selected fault features to form the basic fault diagnosis model. Then the grid search method and genetic algorithm are used. Three parameter optimization algorithms, particle swarm optimization (PSO), are used to optimize the parameters of support vector machine (SVM) diagnosis model in order to obtain accurate location of fault points and effective identification of fault types of gearbox rolling bearings. The simulation results show that, Although the computing speed of grid search method is relatively fast, the accuracy of fault classification is low. Genetic algorithm (GA) is easy to fall into local optimum, and the computation speed is relatively slow, and the classification effect is not good. Particle swarm optimization (PSO) has the highest classification accuracy, faster computation speed than genetic algorithm, but poor convergence.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
本文編號：2334957
[Abstract]:The gearbox is an important part of the transmission system of wind turbine. The rolling bearing of the gearbox is one of the parts with high failure rate in the transmission chain. Bearing failure not only affects the normal operation of the unit, but also affects the safe and stable operation of the power supply network. Therefore, it has important practical significance and practical value to diagnose the bearing fault of wind turbine gearbox quickly. In this paper, the basic structure and failure mechanism of wind turbine are analyzed. Based on the fault features and their characteristic frequencies, the improved threshold method of empirical mode decomposition (Empirical Mode Decomposition) is used to pre-process the vibration signals of rolling bearings in the event of failure, then the fault features are extracted and the fault types are analyzed. Combining time and frequency domain analysis technology with the knowledge of rotating machinery fault theory, this paper aims at the problems of common mechanical faults in the mechanical transmission system of wind turbine, such as tooth breaking, pitting, abrasion, eccentricity, bearing inner ring, outer ring, etc. The problems of rolling body damage and other faults are comprehensively analyzed and studied. The intelligent classification algorithm, support vector machine (Support Vector Machines), is used to analyze and classify the gearbox bearing of wind turbine. The feasibility and accuracy of the fault diagnosis algorithm are verified by the simulated fault data of the measured experiments, which provides a new method for the fault diagnosis of wind turbine generator. The research contents and conclusions are as follows: (1) based on the basic principle of mechanical fault diagnosis, the mechanism of mechanical vibration failure of roller bearing of wind turbine gearbox is analyzed, and the characteristic frequency of each component fault is analyzed. The corresponding fault characteristics of different components are defined, which provides data support and theoretical basis for subsequent fault diagnosis and classification experiment verification. (2) after obtaining the fault test data, wavelet decomposition and EMD decomposition threshold method are used to reduce the noise, the spectrum and envelope spectrum are analyzed, the fault characteristic quantity in the spectrum chart is observed and analyzed, and the improved EMD threshold denoising method is put forward. The feasibility and superiority of the method are verified. (3) the peak-peak value, effective value, variance and kurtosis are selected as the fault characteristic variables in the fault data. The support vector machine (SVM) classification and recognition algorithm is used to train the training samples composed of the selected fault features to form the basic fault diagnosis model. Then the grid search method and genetic algorithm are used. Three parameter optimization algorithms, particle swarm optimization (PSO), are used to optimize the parameters of support vector machine (SVM) diagnosis model in order to obtain accurate location of fault points and effective identification of fault types of gearbox rolling bearings. The simulation results show that, Although the computing speed of grid search method is relatively fast, the accuracy of fault classification is low. Genetic algorithm (GA) is easy to fall into local optimum, and the computation speed is relatively slow, and the classification effect is not good. Particle swarm optimization (PSO) has the highest classification accuracy, faster computation speed than genetic algorithm, but poor convergence.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
【參考文獻】
相關(guān)期刊論文 前10條
1 丁世飛;齊丙娟;譚紅艷;;支持向量機理論與算法研究綜述[J];電子科技大學學報;2011年01期
2 周培毅;張新燕;;風力發(fā)電機組圓柱齒輪的故障振動分析[J];華東電力;2008年06期
3 謝琦;陳維義;林偉;;平移不變小波變換在消除電路噪聲中的應用[J];艦船科學技術(shù);2011年05期
4 張婷婷;楊潔明;;基于改進小波域閾值法的平移不變振動信號去噪[J];機電工程;2009年06期
5 郭洪澈;風力發(fā)電系統(tǒng)的故障診斷專家系統(tǒng)設(shè)計[J];節(jié)能;2002年07期
6 徐玉秀;張承東;;風力機葉片應變響應分形特征及損傷識別研究[J];機械科學與技術(shù);2009年01期
7 湯寶平;蔣永華;張詳春;;基于形態(tài)奇異值分解和經(jīng)驗模態(tài)分解的滾動軸承故障特征提取方法[J];機械工程學報;2010年05期
8 王思文;鄭衛(wèi)剛;;經(jīng)驗模態(tài)分解及其在降噪方面的應用[J];機械制造;2011年10期
9 李德強;吳永國;羅海波;;基于冗余離散小波變換的信號配準及分類[J];自動化學報;2011年01期
10 葉昊,王桂增,方崇智;小波變換在故障檢測中的應用[J];自動化學報;1997年06期
，本文編號：2334957
本文鏈接：http://sikaile.net/kejilunwen/dianlilw/2334957.html
最近更新
教材專著
