基于諧波小波和支持向量機的風電葉片損傷識別研究
發(fā)布時間：2018-08-27 17:03
【摘要】：葉片是風力發(fā)電機的關鍵部件之一，對葉片損傷的研究越來越受到該領域研究人員的關注。由于葉片的結構巨大、形狀不規(guī)則、材料鋪層復雜并且長期工作在惡劣的環(huán)境下，所以當前需要解決的難題是如何實現(xiàn)葉片的健康監(jiān)測。目前常用的監(jiān)測手段是通過監(jiān)測其模態(tài)來判斷葉片的損傷狀況，但該方法的缺點是敏感度低，而且一直未能得到有效地解決。針對這一問題，本文提出利用聲發(fā)射技術對風電葉片損傷狀況進行檢測，并應用SVM(Support Vector Machine,支持向量機)對葉片的兩類損傷模式進行識別。 由于葉片在受到外力破壞時會引起材料內部應變從而產生聲發(fā)射信號，通過對聲發(fā)射信號進行采集和分析，能夠實現(xiàn)對聲發(fā)射信號源的識別。首先接通聲發(fā)射傳感器、信號放大器、數(shù)據采集卡和計算機等設備，搭建聲發(fā)射信號采集實驗平臺，用耦合劑將聲發(fā)射傳感器固定在葉片上。然后人工對靜態(tài)的單個葉片進行加載，模擬葉片的裂紋擴展和邊緣破損兩類損傷，并采集損傷時的聲發(fā)射信號。 采集到信號后，分別利用諧波小波包和db10小波包對聲發(fā)射信號進行4層分解并計算信號的各頻段能量值，，將所得能量值進行歸一化處理后，所得數(shù)據作為特征向量，采用SVM對特征向量進行訓練學習，建立葉片損傷識別模型。在進行葉片的損傷識別時，對兩種小波包的特征提取效果進行了比較，仿真結果表明，采用諧波小波包和SVM結合的方法可以獲得良好的識別效果。該方法能夠有效地識別不同類型的損傷，有助于發(fā)現(xiàn)葉片初期損傷，使葉片可以得到及時地維護，防止損傷的進一步擴展。
[Abstract]:Blade is one of the key components of wind turbine. Because of the huge structure, irregular shape, complicated material layer and long term working environment, the problem that needs to be solved is how to realize the blade health monitoring. At present, the commonly used monitoring method is to judge the damage condition of the blade by monitoring its mode, but the disadvantage of this method is that the sensitivity is low, and it has not been effectively solved. In order to solve this problem, the acoustic emission technique is used to detect the damage of wind turbine blades, and SVM (Support Vector Machine, support vector machine (SVM) is applied to identify the two types of damage patterns. The acoustic emission signal can be obtained by collecting and analyzing the acoustic emission signal because the blade will cause internal strain of the material when it is damaged by external force, and the acoustic emission signal source can be recognized. First, the acoustic emission sensor, signal amplifier, data acquisition card and computer are connected to build the experimental platform of acoustic emission signal acquisition, and the acoustic emission sensor is fixed on the blade with coupling agent. Then, the static single blade is loaded manually to simulate the crack propagation and edge damage of the blade, and the acoustic emission signals are collected. After collecting the signal, the harmonic wavelet packet and the db10 wavelet packet are used to decompose the acoustic emission signal into four layers and calculate the energy values of each frequency band of the signal. After normalizing the energy value, the obtained data is used as the eigenvector. The feature vector is trained and studied by SVM, and the model of blade damage identification is established. In the process of blade damage identification, the feature extraction effects of two kinds of wavelet packets are compared. The simulation results show that the method of harmonic wavelet packet and SVM can obtain good recognition effect. This method can effectively identify different types of damage, help to detect the initial damage of leaves, enable the leaves to be maintained in time, and prevent the damage from spreading further.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
本文編號：2207890
[Abstract]:Blade is one of the key components of wind turbine. Because of the huge structure, irregular shape, complicated material layer and long term working environment, the problem that needs to be solved is how to realize the blade health monitoring. At present, the commonly used monitoring method is to judge the damage condition of the blade by monitoring its mode, but the disadvantage of this method is that the sensitivity is low, and it has not been effectively solved. In order to solve this problem, the acoustic emission technique is used to detect the damage of wind turbine blades, and SVM (Support Vector Machine, support vector machine (SVM) is applied to identify the two types of damage patterns. The acoustic emission signal can be obtained by collecting and analyzing the acoustic emission signal because the blade will cause internal strain of the material when it is damaged by external force, and the acoustic emission signal source can be recognized. First, the acoustic emission sensor, signal amplifier, data acquisition card and computer are connected to build the experimental platform of acoustic emission signal acquisition, and the acoustic emission sensor is fixed on the blade with coupling agent. Then, the static single blade is loaded manually to simulate the crack propagation and edge damage of the blade, and the acoustic emission signals are collected. After collecting the signal, the harmonic wavelet packet and the db10 wavelet packet are used to decompose the acoustic emission signal into four layers and calculate the energy values of each frequency band of the signal. After normalizing the energy value, the obtained data is used as the eigenvector. The feature vector is trained and studied by SVM, and the model of blade damage identification is established. In the process of blade damage identification, the feature extraction effects of two kinds of wavelet packets are compared. The simulation results show that the method of harmonic wavelet packet and SVM can obtain good recognition effect. This method can effectively identify different types of damage, help to detect the initial damage of leaves, enable the leaves to be maintained in time, and prevent the damage from spreading further.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
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