【摘要】：隨著風(fēng)電產(chǎn)業(yè)的發(fā)展,風(fēng)力發(fā)電機(jī)組的穩(wěn)定安全運(yùn)行和故障診斷越來越受到科學(xué)研究者的注意。齒輪箱是風(fēng)機(jī)傳動(dòng)鏈的一個(gè)重要組成部件,它在運(yùn)行中會(huì)受多種因素影響;齒輪箱一旦發(fā)生故障,就可能引發(fā)風(fēng)機(jī)傳動(dòng)鏈的崩潰。因此,齒輪箱的故障診斷研究對于維持風(fēng)機(jī)的正常運(yùn)行具有重要意義。本文的主要研究內(nèi)容是風(fēng)機(jī)齒輪箱多故障診斷,為了解決這個(gè)問題,文章提出了兩種不同的解決方案:1、本文提出了一種新的欠定盲源分離算法來解決齒輪箱多故障診斷問題。該算法將盲源分離問題分解為兩個(gè)子問題,即源信號數(shù)目估計(jì)和源信號恢復(fù)。源信號數(shù)目由經(jīng)驗(yàn)?zāi)B(tài)分解(empirical mode decomposition,EMD)、奇異值分解(singular value decomposition,SVD)和 K 均值(K-means)聚類聯(lián)合算法估計(jì)。然后,輸入信號通過短時(shí)傅立葉變換轉(zhuǎn)換到時(shí)-頻域。最后,通過模糊C聚類估計(jì)混疊矩陣,恢復(fù)源信號采用的是最小化l1范數(shù)。實(shí)驗(yàn)結(jié)果清晰地驗(yàn)證了算法在處理齒輪箱非線性多故障問題時(shí)的有效性。2、本文的另一種方法為基于支持向量機(jī)(support vector machine,SVM)概率估計(jì)的多故障診斷方法。該方法對安裝在齒輪箱上不同位置的傳感器分別建立支持向量機(jī)模型。每個(gè)模型都會(huì)輸出樣本歸屬于各個(gè)類的概率,最終診斷結(jié)果是這些概率的綜合。為了提高模型的診斷率,方法引入了總體經(jīng)驗(yàn)?zāi)B(tài)分解(ensemble empirical mode decomposition,EEMD)來進(jìn)行特征提取。該算法的有效性經(jīng)仿真數(shù)據(jù)和真實(shí)數(shù)據(jù)驗(yàn)證。
[Abstract]:With the development of wind power industry, more and more scientific researchers pay attention to the stable and safe operation and fault diagnosis of wind turbine. Gear box is an important component of fan transmission chain, it will be affected by many factors in operation, once the gear box failure, it may lead to the failure of fan transmission chain. Therefore, the research on fault diagnosis of gearbox is of great significance for maintaining the normal operation of fan. The main research content of this paper is multi-fault diagnosis of fan gearbox. In order to solve this problem, this paper puts forward two different solutions: 1, In this paper, a new blind source separation algorithm is proposed to solve the problem of multi-fault diagnosis of gearbox. The algorithm decomposes the blind source separation problem into two sub-problems, that is, the estimation of the number of source signals and the restoration of the source signals. The number of source signals is estimated by the combined empirical mode decomposition (empirical mode decomposition,EMD), singular value decomposition (singular value decomposition,SVD) and K-means (K-means) clustering algorithms. Then, the input signal is converted to time-frequency domain by short-time Fourier transform. Finally, the aliasing matrix is estimated by fuzzy C clustering, and the minimum L1 norm is used to recover the source signal. The experimental results clearly verify the effectiveness of the algorithm in dealing with the nonlinear multi-fault problem of gearbox. 2. Another method in this paper is the multi-fault diagnosis method based on support vector machine (support vector machine,SVM) probability estimation. The support vector machine (SVM) models for sensors installed in different locations of gearbox are established by this method. Each model outputs the probability that the sample belongs to each class, and the final diagnosis result is a synthesis of these probabilities. In order to improve the diagnostic rate of the model, the ensemble empirical mode decomposition (ensemble empirical mode decomposition,EEMD) is introduced to extract the features. The validity of the algorithm is verified by simulation data and real data.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM315

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本文編號：2456616