【摘要】：近年來(lái),起重機(jī)械在國(guó)民經(jīng)濟(jì)中的影響越來(lái)越大,其安全問(wèn)題一直受到人們的廣泛關(guān)注。一旦事故發(fā)生,對(duì)事故性質(zhì)的認(rèn)定往往存在一定的難度。其中最易發(fā)生故障的零部件之一就是起重機(jī)齒輪箱,本文主要對(duì)起重機(jī)齒輪箱故障特征提取方法進(jìn)行了研究。齒輪箱被普遍應(yīng)用于機(jī)械設(shè)備傳動(dòng)中,作為連接和傳遞動(dòng)力的部件,齒輪的磨損、裂紋和斷齒故障都會(huì)導(dǎo)致機(jī)器運(yùn)行不正常,因此在線準(zhǔn)確監(jiān)測(cè)和診斷齒輪箱的故障是十分必要的。由于齒輪箱振動(dòng)信號(hào)的非線性、非平穩(wěn)特性,當(dāng)齒輪出現(xiàn)故障時(shí),通常都有較強(qiáng)的背景噪聲存在,這樣會(huì)影響齒輪箱故障診斷的準(zhǔn)確性。本文首先利用在傳統(tǒng)軟、硬閾值方法基礎(chǔ)上改進(jìn)的小波閾值方法,對(duì)采集的齒輪箱振動(dòng)信號(hào)進(jìn)行降噪預(yù)處理,利用EMD方法對(duì)信號(hào)分解,將分解后得到的信號(hào)進(jìn)行譜分析,結(jié)合齒輪故障振動(dòng)信號(hào)的調(diào)制頻率及其邊頻帶分布特點(diǎn),實(shí)現(xiàn)齒輪故障診斷分析,最后引入BP神經(jīng)網(wǎng)絡(luò)故障識(shí)別方法,能夠準(zhǔn)確的識(shí)別齒輪故障狀態(tài)。本文主要的研究?jī)?nèi)容和結(jié)果包括:(1)研究齒輪故障表現(xiàn)出的常見(jiàn)損傷形式及其產(chǎn)生緣由,從而能夠準(zhǔn)確地判斷此故障檢測(cè)參數(shù)的有效性;基于齒輪發(fā)生故障時(shí)出現(xiàn)嚙合頻率調(diào)制和邊頻帶分布現(xiàn)象,得到齒輪典型故障和相應(yīng)振動(dòng)信號(hào)的特征頻率之間的關(guān)系。(2)為抑制齒輪故障信號(hào)中噪聲的干擾,突出故障特征頻率,本文利用一種改進(jìn)的小波閾值降噪方法,通過(guò)對(duì)加噪信號(hào)仿真實(shí)驗(yàn)驗(yàn)證與傳統(tǒng)的軟、硬閾值降噪方法進(jìn)行了對(duì)比,證明了此降噪方法的有效性。(3)將改進(jìn)小波分析閾值法和EMD方法相結(jié)合分析振動(dòng)信號(hào)。綜合對(duì)比齒輪箱在不同故障狀態(tài)下的振動(dòng)信號(hào)的時(shí)域波形、幅值譜、Hilbert譜和邊際譜,獲得齒輪箱在不同故障狀態(tài)下的故障特征頻率及其附近調(diào)制邊頻帶特征,成功完成齒輪箱故障診斷分析。(4)引入BP神經(jīng)網(wǎng)絡(luò),利用EMD提取的相應(yīng)特征向量,作為神經(jīng)網(wǎng)絡(luò)的訓(xùn)練樣本和測(cè)試樣本。通過(guò)對(duì)BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)和識(shí)別,能夠分類出相應(yīng)的工作狀態(tài),對(duì)齒輪箱的相應(yīng)故障做出判定。實(shí)驗(yàn)表明此方法適合于齒輪箱故障識(shí)別。
[Abstract]:In recent years, lifting machinery has become more and more important in the national economy, and its safety has been paid more and more attention. Once an accident occurs, it is often difficult to identify the nature of the accident. One of the most prone parts is crane gearbox. In this paper, the fault feature extraction method of crane gearbox is studied. Gearbox is widely used in mechanical transmission. As a part of connecting and transmitting power, gear wear, crack and broken tooth failure will lead to abnormal operation of the machine. Therefore, it is necessary to accurately monitor and diagnose the gearbox faults on line. Because of the nonlinear and non-stationary characteristics of the gear box vibration signal, there is usually a strong background noise when the gear is in trouble, which will affect the accuracy of the gear box fault diagnosis. In this paper, the wavelet threshold method, which is based on the traditional soft and hard threshold method, is firstly used to pre-process the vibration signal of the gearbox. The signal is decomposed by EMD method, and the decomposed signal is analyzed by spectrum analysis. According to the modulation frequency of gear fault vibration signal and the characteristics of frequency band distribution, the gear fault diagnosis and analysis is realized. Finally, the fault identification method of BP neural network is introduced, which can accurately identify the gear fault state. The main contents and results of this paper are as follows: (1) the common damage forms and their causes of gear failure are studied, so that the validity of the fault detection parameters can be accurately judged; Based on the phenomenon of meshing frequency modulation and side band distribution when gear fault occurs, the relationship between the characteristic frequency of gear typical fault and corresponding vibration signal is obtained. (2) in order to suppress the interference of noise in gear fault signal, Highlighting the characteristic frequency of fault, this paper uses an improved wavelet threshold denoising method, and compares it with the traditional soft and hard threshold denoising method through the simulation of the noise-added signal. It is proved that this method is effective. (3) the improved wavelet analysis threshold method and the EMD method are combined to analyze the vibration signal. The time domain waveform, amplitude spectrum, Hilbert spectrum and marginal spectrum of vibration signal of gearbox in different fault state are compared synthetically, and the fault characteristic frequency and the modulation side band characteristic of gearbox in different fault state are obtained. The analysis of gearbox fault diagnosis is completed successfully. (4) the BP neural network is introduced and the corresponding eigenvector extracted by EMD is used as the training sample and test sample of the neural network. By learning and recognizing the BP neural network, the corresponding working states can be classified and the corresponding faults of the gearbox can be judged. Experiments show that this method is suitable for gearbox fault identification.
【學(xué)位授予單位】：上海應(yīng)用技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TH21

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張曉楠;曾慶山;萬(wàn)紅;;基于改進(jìn)小波去噪和EMD方法的軸承故障診斷[J];測(cè)控技術(shù);2014年01期

2 賀文杰;Bajole Jtulien;Yoann Plassard;陳漢新;魯艷軍;;基于EMD和FFT的齒輪箱故障診斷[J];武漢工程大學(xué)學(xué)報(bào);2011年01期

3 馬晶;;Wigner-Ville分布及其在故障診斷中的應(yīng)用[J];儀表技術(shù);2011年01期

4 郭曉霞;楊慧中;;小波去噪中軟硬閾值的一種改良折衷法[J];智能系統(tǒng)學(xué)報(bào);2008年03期

5 陳剛;廖明夫;;基于小波分析的滾動(dòng)軸承故障診斷研究[J];科學(xué)技術(shù)與工程;2007年12期

6 戴桂平;劉彬;;基于小波去噪和EMD的信號(hào)瞬時(shí)參數(shù)提取[J];計(jì)量學(xué)報(bào);2007年02期

7 劉仁生;齒輪的振動(dòng)故障研究[J];中國(guó)安全科學(xué)學(xué)報(bào);2005年02期

8 李天云,趙妍,李楠;基于EMD的Hilbert變換應(yīng)用于暫態(tài)信號(hào)分析[J];電力系統(tǒng)自動(dòng)化;2005年04期

9 崔玉杰;典型齒輪箱故障振動(dòng)特征與診斷策略研究[J];天津冶金;2004年05期

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