本文關(guān)鍵詞：北票風電場發(fā)電機組的齒輪箱故障診斷研究　出處：《遼寧工程技術(shù)大學》2011年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 故障診斷 遺傳算法(GA) 模糊規(guī)則 模糊神經(jīng)網(wǎng)絡(FNN)

【摘要】：齒輪箱是風力發(fā)電機組的重要組成部分,如何及早發(fā)現(xiàn)并診斷齒輪的故障,對維護系統(tǒng)正常運行,經(jīng)濟合理地安排維修設(shè)備時間,減少設(shè)備故障發(fā)生,避免重大人身傷亡事故有著十分重要的意義。 故障診斷方法很多,諸如:傳統(tǒng)故障診斷、數(shù)學故障診斷、智能故障診斷方法(模糊邏輯、神經(jīng)網(wǎng)絡、專家系統(tǒng))等。通過綜合比較,本文提出了基于遺傳算法(GA)的模糊神經(jīng)網(wǎng)絡模型(FNN),并通過在神經(jīng)網(wǎng)絡框架下引入模糊規(guī)則,從而使網(wǎng)絡權(quán)值有明顯意義,并且保留神經(jīng)網(wǎng)絡的學習機制。使用遺傳算法在搜索解的過程中,能夠達到最佳收斂,優(yōu)化全局。在神經(jīng)網(wǎng)絡訓練之前,引入GA對染色體的交叉、變異運算尋找BP網(wǎng)絡的最優(yōu)初始權(quán)值,訓練網(wǎng)絡時再次引入GA優(yōu)化網(wǎng)絡參數(shù),可以有效避免網(wǎng)絡收斂過早。本文針對權(quán)值的學習采用進化算法,避免了原有BP算法容易陷入局部最優(yōu)的缺點。 本文首先使用了模糊規(guī)則專家系統(tǒng),對齒輪箱進行故障診斷,得出診斷結(jié)果,本文分析了這種方法在故障診斷中具有實用性,同時也存在很大的局限性.由于神經(jīng)網(wǎng)絡強大的學習能力,被廣泛應用與故障診斷。在文章中采用了BP網(wǎng)絡建立故障診斷模型和基于遺傳算法、神經(jīng)網(wǎng)絡、模糊邏輯結(jié)合建立模糊神經(jīng)網(wǎng)絡模型,對齒輪箱故障進行診斷,均可以得到正確的故障診斷結(jié)果。通過對于兩種方法訓練時間,相對誤差值等方面的比較,顯示了GA-FNN的優(yōu)越性,表明了該方法的有效性、可行性,達到了預期效果。
[Abstract]:Gearbox is an important part of wind turbine. How to detect and diagnose the fault of gear as early as possible, to the normal operation of the maintenance system, to arrange the maintenance equipment time economically and reasonably, and to reduce the fault of the equipment. It is of great significance to avoid serious personal injury and injury. There are many fault diagnosis methods, such as: traditional fault diagnosis, mathematical fault diagnosis, intelligent fault diagnosis (fuzzy logic, neural network, expert system). In this paper, a fuzzy neural network model based on genetic algorithm (GA) is proposed, and the fuzzy rules are introduced under the framework of neural network to make the weight value of the network have obvious significance. The genetic algorithm can achieve the best convergence and optimize the whole situation in the process of searching the solution. Before the neural network training, the genetic algorithm is introduced to the crossover of chromosomes. Mutation operation can find the optimal initial weight of BP network, and introduce GA to optimize the network parameters again when training the network, which can effectively avoid premature convergence of the network. In this paper, evolutionary algorithm is used to study the weights. It avoids the disadvantage that the original BP algorithm is easy to fall into local optimum. In this paper, a fuzzy rule expert system is first used to diagnose the gearbox, and the result is obtained. This paper analyzes the practicability of this method in fault diagnosis. At the same time, there are also great limitations. Because of the powerful learning ability of neural network, it is widely used and fault diagnosis. In this paper, BP neural network is used to establish fault diagnosis model and based on genetic algorithm, neural network. Fuzzy logic combined with fuzzy neural network model can be used to diagnose the gearbox fault, and the correct fault diagnosis results can be obtained. The training time and the relative error of the two methods are compared. The superiority of GA-FNN is demonstrated, and the effectiveness and feasibility of this method are demonstrated.
【學位授予單位】：遼寧工程技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關(guān)碩士學位論文 前1條

1 馬濤;基于振動信號的大型風力發(fā)電機齒輪箱健康狀態(tài)預測研究[D];沈陽工業(yè)大學;2013年

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本文編號：1379970