【摘要】：近年來,起重機械在國民經(jīng)濟中的影響越來越大,其安全問題一直受到人們的廣泛關(guān)注。一旦事故發(fā)生,對事故性質(zhì)的認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齒輪箱,本文主要對起重機齒輪箱故障特征提取方法進行了研究。齒輪箱被普遍應(yīng)用于機械設(shè)備傳動中,作為連接和傳遞動力的部件,齒輪的磨損、裂紋和斷齒故障都會導(dǎo)致機器運行不正常,因此在線準確監(jiān)測和診斷齒輪箱的故障是十分必要的。由于齒輪箱振動信號的非線性、非平穩(wěn)特性,當(dāng)齒輪出現(xiàn)故障時,通常都有較強的背景噪聲存在,這樣會影響齒輪箱故障診斷的準確性。本文首先利用在傳統(tǒng)軟、硬閾值方法基礎(chǔ)上改進的小波閾值方法,對采集的齒輪箱振動信號進行降噪預(yù)處理,利用EMD方法對信號分解,將分解后得到的信號進行譜分析,結(jié)合齒輪故障振動信號的調(diào)制頻率及其邊頻帶分布特點,實現(xiàn)齒輪故障診斷分析,最后引入BP神經(jīng)網(wǎng)絡(luò)故障識別方法,能夠準確的識別齒輪故障狀態(tài)。本文主要的研究內(nèi)容和結(jié)果包括:(1)研究齒輪故障表現(xiàn)出的常見損傷形式及其產(chǎn)生緣由,從而能夠準確地判斷此故障檢測參數(shù)的有效性;基于齒輪發(fā)生故障時出現(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)振動信號的特征頻率之間的關(guān)系。(2)為抑制齒輪故障信號中噪聲的干擾,突出故障特征頻率,本文利用一種改進的小波閾值降噪方法,通過對加噪信號仿真實驗驗證與傳統(tǒng)的軟、硬閾值降噪方法進行了對比,證明了此降噪方法的有效性。(3)將改進小波分析閾值法和EMD方法相結(jié)合分析振動信號。綜合對比齒輪箱在不同故障狀態(tài)下的振動信號的時域波形、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)練樣本和測試樣本。通過對BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)和識別,能夠分類出相應(yīng)的工作狀態(tài),對齒輪箱的相應(yīng)故障做出判定。實驗表明此方法適合于齒輪箱故障識別。
[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)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TH21

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本文編號：2310259