本文選題：風(fēng)力發(fā)電機(jī) + 故障診斷��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：風(fēng)能是儲(chǔ)存量豐富的可再生潔凈能源,是人類最早利用的能源之一。在利用它轉(zhuǎn)換為電能的過程中,由于不產(chǎn)生有害氣體和廢料,也就是說它不污染環(huán)境,因此受到世界各國(guó)政府的廣泛重視。隨著風(fēng)能技術(shù)的快速發(fā)展和日趨完善,風(fēng)力發(fā)電機(jī)的可靠性越來(lái)越高,但是在風(fēng)力發(fā)電系統(tǒng)的高速發(fā)展的同時(shí),風(fēng)力發(fā)電機(jī)故障問題也收到了極大的關(guān)注,如風(fēng)機(jī)自身的軸承磨損、齒輪斷齒、軸偏心等常見故障,都可能造成風(fēng)機(jī)毀壞,從而降低經(jīng)濟(jì)效益,鑒于這些,風(fēng)機(jī)故障診斷已經(jīng)逐漸成為風(fēng)力發(fā)電發(fā)展中的重要研究?jī)?nèi)容。風(fēng)力發(fā)電機(jī)由于長(zhǎng)時(shí)間地工作,一些部件之間的摩擦就會(huì)使得儀器的部件老化或者磨損,這無(wú)疑降低了發(fā)電機(jī)的壽命,降低了發(fā)電機(jī)的工作效率,而最常見的故障就是齒輪斷齒和軸承的磨損,而對(duì)這些故障的故障診斷方法有直接觀察法、振動(dòng)和噪聲檢測(cè)法、無(wú)損檢測(cè)法、磨損殘余物檢測(cè)法、機(jī)器性能參數(shù)檢測(cè)法等。為了對(duì)這些故障進(jìn)行高效地診斷,傳統(tǒng)的直接觀察法判斷已經(jīng)不足以滿足風(fēng)力發(fā)電機(jī)的發(fā)展和工作要求,所以一般是利用振動(dòng)檢測(cè)法,振動(dòng)信號(hào)是機(jī)械設(shè)備狀態(tài)信息的載體,包含了豐富的故障特征信息,故障診斷就是通過各種信號(hào)處理方法,把隱藏在振動(dòng)信號(hào)中的有意義的特征信息提取出來(lái),實(shí)現(xiàn)對(duì)設(shè)備的診斷。因此采用風(fēng)力發(fā)電機(jī)振動(dòng)信號(hào)判斷其故障是一種可靠的方式,而對(duì)振動(dòng)信號(hào)最合適的處理方式就是信號(hào)的時(shí)頻分析方法,傳統(tǒng)的時(shí)頻分析方法如短時(shí)傅立葉變換、Wigner-Ville分布等,它們分別存在著窗效應(yīng)和交叉項(xiàng)的問題且都不是自適應(yīng)的。本文研究了一種改進(jìn)的Hilbert-Huang變換(HHT),并將這改進(jìn)的時(shí)頻分析方法運(yùn)用到風(fēng)力發(fā)電機(jī)故障信號(hào)處理中。Hilbert_Huang變換通過經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)的方式將信號(hào)分解成為有限個(gè)固有模態(tài)(IMF),每一個(gè)固有模態(tài)都是一個(gè)穩(wěn)態(tài)的信號(hào),因此可以對(duì)每一個(gè)固有模態(tài)進(jìn)行Hilbert變換,得到了信號(hào)的Hilbert譜,從而得到了信號(hào)的特征,結(jié)合模糊神經(jīng)網(wǎng)絡(luò)系統(tǒng),對(duì)故障的類型進(jìn)行更加準(zhǔn)確和方便地識(shí)別。但是傳統(tǒng)的Hilbert_Huang變換存在著端點(diǎn)飛翼和終止條件的問題,本文針對(duì)這兩個(gè)缺陷進(jìn)行了一系列的研究,最終形成了了一個(gè)種改進(jìn)的HHT分析方法,利用這種改進(jìn)的HHT分析方法獲得了發(fā)電機(jī)的振動(dòng)信號(hào)的時(shí)頻特征,但是HHT方法只是得到信號(hào)的時(shí)頻特征,但是并沒得到最后的故障診斷結(jié)果,在目前的研究中,模糊神經(jīng)網(wǎng)絡(luò)已經(jīng)得到了較快發(fā)展,并且顯示出了強(qiáng)大的優(yōu)勢(shì),它是一種新的診斷和識(shí)別技術(shù),它將模糊邏輯推理的強(qiáng)大結(jié)構(gòu)性知識(shí)表達(dá)能力和神經(jīng)網(wǎng)絡(luò)的強(qiáng)大自學(xué)習(xí)能力結(jié)合為了一體,本文將HHT分析得到的時(shí)頻特征結(jié)合了模糊神經(jīng)網(wǎng)絡(luò)進(jìn)行故障類型的診斷和識(shí)別,獲得了很好的效果。
[Abstract]:Wind energy is a renewable clean energy with abundant storage, and it is one of the earliest energy sources used by human beings. In the process of using it to convert electric energy, because it does not produce harmful gas and waste, that is to say, it does not pollute the environment, so the governments all over the world pay more attention to it. With the rapid development and improvement of wind energy technology, the reliability of wind turbine is becoming more and more high. However, with the rapid development of wind power system, the problem of wind turbine fault has also received great attention. Such as bearing wear, gear tooth breaking, shaft eccentricity and other common faults may cause fan damage, thus reducing economic benefits. In view of these, fan fault diagnosis has gradually become an important research content in the development of wind power generation. Because wind turbines work for a long time, friction between some parts will cause the parts of the instrument to age or wear, which undoubtedly reduces the life of the generator and reduces the efficiency of the generator. The most common fault is the wear of gear broken teeth and bearing. The fault diagnosis methods of these faults include direct observation method, vibration and noise detection method, nondestructive testing method, wear residue detection method, machine performance parameter detection method and so on. In order to diagnose these faults efficiently, the traditional direct observation method is not enough to meet the development and working requirements of wind turbine, so the vibration signal is the carrier of mechanical equipment status information. Fault diagnosis is to extract the meaningful feature information hidden in the vibration signal through various signal processing methods to realize the diagnosis of the equipment. Therefore, it is a reliable way to use vibration signal of wind turbine to judge its fault, and the most suitable way to deal with vibration signal is time-frequency analysis method, traditional time-frequency analysis method such as short time Fourier transform Wigner-Ville distribution, etc. They have the problem of window effect and crossover, respectively, and they are not adaptive. In this paper, an improved Hilbert-Huang transform is studied, and the improved time-frequency analysis method is applied to wind turbine fault signal processing. Hilbert Huang transform decomposes the signal into finite inherent modes by empirical mode decomposition (EMD). IMF, each inherent mode is a steady-state signal, Therefore, the Hilbert transform can be carried out for each inherent mode, and the Hilbert spectrum of the signal can be obtained, thus the characteristics of the signal can be obtained, and the fault type can be identified more accurately and conveniently with the combination of the fuzzy neural network system. However, the traditional Hilbert_Huang transform has the problems of terminal wing and termination condition. In this paper, a series of research on these two defects has been carried out, and an improved HHT analysis method has been developed. The improved HHT analysis method is used to obtain the time-frequency characteristics of the generator vibration signals, but the HHT method only obtains the time-frequency characteristics of the signals, but the final fault diagnosis results are not obtained. Fuzzy neural network (FNN) has been developed rapidly and has shown strong advantages. It is a new diagnosis and recognition technology. It combines the strong structural knowledge expression ability of fuzzy logic reasoning with the powerful self-learning ability of neural network. In this paper, the time-frequency features obtained by HHT analysis are combined with fuzzy neural network to diagnose and identify fault types. Good results have been achieved.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM315

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 李輝;滾動(dòng)軸承和齒輪振動(dòng)信號(hào)分析與故障診斷方法[D];西北工業(yè)大學(xué);2001年

2 陸曉來(lái);基于模糊神經(jīng)網(wǎng)絡(luò)的移動(dòng)機(jī)器人避障研究[D];東北大學(xué);2010年

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本文編號(hào)：1859288