【摘要】：旋轉(zhuǎn)機(jī)械設(shè)備在工作時(shí)的振動(dòng)參數(shù)信號(hào)，特別在設(shè)備存在故障的條件下，絕大多數(shù)都是非平穩(wěn)信號(hào)，其信號(hào)特征中頻率是隨時(shí)間而變化的，如果只在時(shí)域或頻域中分析是遠(yuǎn)遠(yuǎn)是不夠的，需掌握非平穩(wěn)信號(hào)在頻率—時(shí)間尺度中幅值或能量分布情況。 故障特征提取過程是旋轉(zhuǎn)機(jī)械故障診斷中的最關(guān)鍵且重要的問題，考慮旋轉(zhuǎn)機(jī)械系統(tǒng)非平穩(wěn)信號(hào)特征，對(duì)比其他時(shí)頻分析方法，本文對(duì)現(xiàn)今非平穩(wěn)信號(hào)故障特征提取方法研究熱點(diǎn)—基于希爾伯特黃變換時(shí)頻分析方法進(jìn)行了深入的研究，總結(jié)經(jīng)驗(yàn)?zāi)Ｊ椒纸獾乃惴ù嬖诙它c(diǎn)效應(yīng)和模態(tài)混疊的問題。針對(duì)旋轉(zhuǎn)機(jī)械現(xiàn)場(chǎng)信號(hào)往往參雜大量隨機(jī)噪聲和脈沖干擾問題，對(duì)奇異值分解和形態(tài)濾波理論進(jìn)行研究，結(jié)合奇異值分解可有效消除隨機(jī)噪聲和形態(tài)濾波能較好抑制脈沖干擾特點(diǎn)，提出了奇異形態(tài)濾波去噪方法，該方法可有效消除現(xiàn)場(chǎng)測(cè)量中參雜的隨機(jī)噪聲和脈沖干擾，避免經(jīng)驗(yàn)分解的模態(tài)混疊現(xiàn)象。針對(duì)經(jīng)驗(yàn)?zāi)Ｊ椒纸庠趯?shí)際使用中存在端點(diǎn)效應(yīng)和模態(tài)混疊等問題，提出了適用于旋轉(zhuǎn)機(jī)械非平穩(wěn)信號(hào)的微弱故障特征提取方法—集總極值域均值分解算法，該方法可有效解決經(jīng)驗(yàn)?zāi)Ｊ椒纸馑惴ǖ木窒扌浴?最后，采用QPZZ-II旋轉(zhuǎn)機(jī)械振動(dòng)故障模擬實(shí)驗(yàn)平臺(tái)，模擬滾動(dòng)軸承故障形式，分別模擬滾動(dòng)軸承的外圈損失故障、內(nèi)圈損傷故障和滾動(dòng)體損傷故障進(jìn)行，，對(duì)本文提出的方法進(jìn)行試驗(yàn)驗(yàn)證，試驗(yàn)結(jié)果表明，本文方法對(duì)強(qiáng)噪聲非平穩(wěn)振動(dòng)信號(hào)能夠有效提取出故障特征頻率。
[Abstract]:The vibration parameter signals of rotating machinery and equipment, especially under the condition of equipment failure, are mostly non-stationary signals, and the frequency of the signals is changed with time. If it is not enough to analyze only in time domain or frequency domain, it is necessary to master the amplitude or energy distribution of non-stationary signal in frequency-time scale. Fault feature extraction is the most critical and important problem in rotating machinery fault diagnosis. Considering the non-stationary signal characteristics of rotating machinery system, compared with other time-frequency analysis methods, fault feature extraction process is the most important problem in rotating machinery fault diagnosis. In this paper, the current research focus of fault feature extraction for non-stationary signals, time-frequency analysis method based on Hilbert-Huang transform, is deeply studied, and the problem of endpoint effect and modal aliasing in empirical mode decomposition algorithm is summarized. The singular value decomposition (SVD) and morphological filtering theory are studied to solve the problem of random noise and pulse interference in rotating machinery field signals. Combined with singular value decomposition (SVD) can effectively eliminate random noise and morphological filter can better suppress the characteristics of pulse interference, a singular morphological filter denoising method is proposed, which can effectively eliminate random noise and pulse interference in field measurement. The mode aliasing phenomenon of empirical decomposition is avoided. In order to solve the problem of endpoint effect and modal aliasing in practical application of empirical mode decomposition, a new method for extracting weak fault feature of non-stationary signals of rotating machinery, called lumped Polar mean decomposition algorithm, is proposed. This method can effectively solve the limitation of empirical mode decomposition algorithm. Finally, QPZZ-II rotating machinery vibration simulation experiment platform is used to simulate the fault form of rolling bearing, which simulates the outer ring loss fault, inner ring damage fault and rolling body damage fault of rolling bearing, respectively. The experimental results show that the proposed method can extract the fault characteristic frequency effectively for the strong noise non-stationary vibration signal.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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