本文選題：機(jī)械故障診斷 + 經(jīng)驗(yàn)小波變換�。� 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：旋轉(zhuǎn)機(jī)械的故障信號(hào)通常是非平穩(wěn)、非線性的含噪振動(dòng)信號(hào),對(duì)于機(jī)械故障診斷目前應(yīng)用較為廣泛的是窗口傅里葉變換、Wigner分布、小波變換(WT)等時(shí)頻分析方法,但這些方法都具有一定的局限性,而且容易受到干擾的影響。經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)較其他方法具有自適應(yīng)分解的優(yōu)勢(shì),但是由于其自身存在模態(tài)混疊效應(yīng)、端點(diǎn)效應(yīng)以及缺乏一定的理論基礎(chǔ),所以在應(yīng)用方面還存在一定問題。而經(jīng)驗(yàn)小波變換(EWT)則既具有EMD的自適應(yīng)性又具有可靠的理論基礎(chǔ),其極大地減弱了 EMD方法中存在模態(tài)混疊現(xiàn)象,克服了端點(diǎn)效應(yīng)問題,在旋轉(zhuǎn)機(jī)械故障診斷中具有較高的應(yīng)用價(jià)值。筆者利用經(jīng)驗(yàn)小波變換的自適應(yīng)性結(jié)合奇異值分解的濾噪特性提出了新的旋轉(zhuǎn)機(jī)械故障診斷方法。文章首先介紹了經(jīng)驗(yàn)小波變換理論,將經(jīng)驗(yàn)小波變換和經(jīng)驗(yàn)?zāi)B(tài)分解對(duì)多模態(tài)混疊含噪信號(hào)的分解結(jié)果進(jìn)行對(duì)比,驗(yàn)證經(jīng)驗(yàn)小波變換較經(jīng)驗(yàn)?zāi)B(tài)分解存在的優(yōu)勢(shì)。然后對(duì)奇異值分解和奇異值包分解理論進(jìn)行深入研究。最后提出EWT-SVD和EWT-SVDP算法,并通過仿真信號(hào)驗(yàn)證算法的有效性。文章將聯(lián)合算法應(yīng)用在旋轉(zhuǎn)機(jī)械故障診斷的實(shí)例分析中,選取軸承、轉(zhuǎn)子、萬向軸作為研究對(duì)象,利用試驗(yàn)對(duì)軸承的內(nèi)圈故障、轉(zhuǎn)子碰摩故障、萬向軸的動(dòng)不平衡故障振動(dòng)信號(hào)進(jìn)行提取,應(yīng)用文章提出的算法對(duì)故障信號(hào)進(jìn)行分析研究,驗(yàn)證聯(lián)合算法對(duì)于工程實(shí)際應(yīng)用的有效性。通過應(yīng)用文章提出的聯(lián)合算法對(duì)仿真信號(hào)和實(shí)際工程的故障信號(hào)分析可知:基于經(jīng)驗(yàn)小波變換結(jié)合奇異值分解(EWT-SVD)的算法表現(xiàn)出很好的自適應(yīng)性和良好的濾噪性能,能夠?qū)⒍嗄B(tài)含噪仿真信號(hào)有效的分解成含有不同頻率特性的信號(hào),并且和單獨(dú)經(jīng)驗(yàn)小波變換方法相比,聯(lián)合算法表現(xiàn)出良好的濾噪特性,通過對(duì)軸承故障信號(hào)、轉(zhuǎn)子碰摩故障信號(hào)、萬向軸動(dòng)不平衡故障信號(hào)分析,基于經(jīng)驗(yàn)小波變換結(jié)合奇異值分解能夠有效地將原始振動(dòng)信號(hào)分解成不同頻帶中的分量信號(hào);基于經(jīng)驗(yàn)小波變換結(jié)合奇異值包分解(EWT-SVDP)相比經(jīng)驗(yàn)小波分解,不但表現(xiàn)出良好的自適應(yīng)性,濾噪性能也有顯著提高,而且對(duì)于信號(hào)的細(xì)節(jié)成分分析表現(xiàn)出極大的優(yōu)勢(shì),對(duì)于工程實(shí)際信號(hào)的處理能力也大大提高。
[Abstract]:The fault signals of rotating machinery are usually non-stationary and nonlinear noise bearing vibration signals. The time frequency analysis methods such as window Fourier transform, Wigner distribution and wavelet transform (WT) are widely used for mechanical fault diagnosis, but these methods all have certain local limit and are easily affected by interference. Empirical mode decomposition (EMD) has the advantages of adaptive decomposition compared with other methods, but because of its own existence of modal aliasing effect, endpoint effect and lack of a certain theoretical basis, there are still some problems in application. The empirical wavelet transform (EWT) has both the adaptability of EMD and a reliable theoretical basis, which greatly weakens the EMD In this method, the phenomenon of modal aliasing has been found to overcome the endpoint effect problem and has high application value in the fault diagnosis of rotating machinery. The author uses the adaptive characteristic of the empirical wavelet transform to combine the filter noise characteristics of singular value decomposition to put forward a new method of diagnosis of rotating machinery fault. The author introduces the theory of empirical wavelet transform, which will be used by Zhang Shouxian. The empirical wavelet transform and empirical mode decomposition are used to compare the decomposition results of multimodal mixed signals. The advantages of empirical wavelet transform are verified. Then the singular value decomposition and the singular packet decomposition theory are studied. Finally, the EWT-SVD and EWT-SVDP algorithms are proposed, and the simulation signals are used to verify the algorithm. In this paper, the joint algorithm is applied to the case analysis of rotating machinery fault diagnosis. Bearing, rotor and universal axis are selected as the research object. The test is used to extract the inner ring fault of the bearing, the rotor rubbing fault, the dynamic unbalance fault vibration signal of the universal axis, and the fault signal is analyzed and researched by the algorithm proposed in this paper. To verify the effectiveness of the combined algorithm for the practical application of the project, the combined algorithm proposed in the application of the paper shows that the algorithm based on the empirical wavelet transform combined with the singular value decomposition (EWT-SVD) shows good adaptability and good filtering performance, and can multimodal denoising. The simulation signal is effectively decomposed into signals with different frequency characteristics. Compared with the independent empirical wavelet transform, the combined algorithm shows good noise filtering characteristics. Through the analysis of the bearing fault signal, the rotor rubbing fault signal, the universal axis dynamic unbalance fault signal analysis, the empirical wavelet transform combined with singular value decomposition can be used. The original vibration signal is effectively decomposed into component signals in different frequency bands; based on empirical wavelet transform combined with singular packet decomposition (EWT-SVDP), compared with empirical wavelet decomposition, not only good adaptability is shown, but also the noise performance is greatly improved, and it has a great advantage for the analysis of the detail component of the signal. The processing ability of the actual signal is also greatly improved.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH17

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