【摘要】：隨著社會(huì)生產(chǎn)的發(fā)展，要求設(shè)備生產(chǎn)效率不斷提高。這就對(duì)液壓系統(tǒng)以及軸承元件的性能提出了更高的要求。因此液壓泵與軸承元件的實(shí)時(shí)故障診斷就更加重要。軸向柱塞泵與滾動(dòng)軸承故障信號(hào)是一種典型的非平穩(wěn)、非線性信號(hào)，這就需要一種適合處理非平穩(wěn)、非線性信號(hào)的方法準(zhǔn)確地提取故障特征。EMD分解與小波脊線在處理這類信號(hào)中具有其獨(dú)特的優(yōu)勢。 經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)可以把原始信號(hào)分解為一系列固有模態(tài)函數(shù)(IMF)之和。各個(gè)IMF分量起到了對(duì)數(shù)據(jù)特征放大的效果，對(duì)各個(gè)IMF分量進(jìn)行研究能夠更清楚的發(fā)現(xiàn)故障特征。小波脊線基于小波變換理論，是由在時(shí)間—頻率面上滿足每個(gè)時(shí)刻點(diǎn)小波系數(shù)的模極大值點(diǎn)所形成的集合。這些點(diǎn)往往更能清楚地表征故障的特征信息。因此把經(jīng)驗(yàn)?zāi)B(tài)分解與小波脊線結(jié)合起來對(duì)故障信號(hào)的分析能夠更清楚地分析故障信息。 為了驗(yàn)證該方法的有效性和優(yōu)越性，本文對(duì)斜盤式軸向柱塞泵采集的振動(dòng)信號(hào)與美國Case Western Reserve University軸承故障模擬試驗(yàn)臺(tái)采集的軸承故障振動(dòng)信號(hào)進(jìn)行了分析研究。通過邊際譜對(duì)比提取故障信號(hào)EMD分解后的敏感IMF分量進(jìn)行小波脊線包絡(luò)解調(diào)分析，準(zhǔn)確地提取了液壓泵以及滾動(dòng)軸承各種狀態(tài)時(shí)的敏感頻率。并通過敏感IMF分量小波脊線解調(diào)后的時(shí)頻譜與Hilbert變換解調(diào)時(shí)頻譜進(jìn)行了對(duì)比，證明了該方法較Hilbert變換解調(diào)具有更高的時(shí)頻定位精度。 本文提出了一種基于EMD分解的小波脊線解調(diào)信號(hào)能量特征向量提取方法，通過EMD分解后敏感IMF分量進(jìn)行小波脊線解調(diào)得到包絡(luò)信號(hào)，對(duì)降低采樣頻率后的包絡(luò)信號(hào)再次進(jìn)行EMD分解，利用再次分解后的IMF分量信號(hào)能量有效地提取了故障特征向量。利用K均值聚類方法對(duì)液壓泵與軸承的各種狀態(tài)進(jìn)行了故障模式識(shí)別，通過與Hilbert變換解調(diào)提取的特征向量對(duì)比證明了該方法具有一定的優(yōu)勢。
[Abstract]:With the development of social production, equipment production efficiency has been improved. This puts forward higher requirements for the performance of hydraulic system and bearing components. Therefore, the real-time fault diagnosis of hydraulic pump and bearing components is more important. The fault signal of axial piston pump and rolling bearing is a typical nonstationary and nonlinear signal. EMD decomposition and wavelet ridge have unique advantages in processing such signals. Empirical mode decomposition (EMD) can decompose the original signal into the sum of a series of intrinsic modal functions (IMF). Each IMF component has the effect of magnifying the data features, and the study of each IMF component can find fault features more clearly. The wavelet ridge is based on the wavelet transform theory and is a set of modulus maximum points which satisfy the wavelet coefficients at every time point on the time-frequency plane. These points can more clearly represent the characteristic information of the fault. Therefore, the fault signal can be analyzed more clearly by combining empirical mode decomposition with wavelet ridge. In order to verify the effectiveness and superiority of this method, the vibration signals collected by oblique disc axial piston pump and bearing fault vibration signal collected by Case Western Reserve University bearing fault simulator in USA are analyzed and studied in this paper. The sensitive IMF component of fault signal after EMD decomposition is extracted by edge spectrum contrast and the wavelet ridge envelope demodulation analysis is carried out. The sensitive frequency of hydraulic pump and rolling bearing is accurately extracted. The time-frequency spectrum of wavelet ridge demodulation of sensitive IMF component is compared with that of Hilbert transform demodulation. It is proved that this method has higher time-frequency localization accuracy than Hilbert transform demodulation. In this paper, an energy eigenvector extraction method for wavelet ridge demodulation signal based on EMD decomposition is proposed. The envelope signal is obtained by demodulating the sensitive IMF component of wavelet ridge by EMD decomposition. The envelope signal with lower sampling frequency is decomposed by EMD again, and the fault eigenvector is extracted effectively by using the energy of the IMF component signal after being decomposed again. The K-means clustering method is used to identify the fault patterns of hydraulic pump and bearing. The comparison with the eigenvector extracted by Hilbert transform and demodulation proves that this method has some advantages.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3;TN911.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫庚;馮艷紅;郭顯久;張春平;;K-means聚類算法研究[J];長春師范學(xué)院學(xué)報(bào);2011年02期

2 趙協(xié)廣;戴炬;;基于EMD分解與小波包的滾動(dòng)軸承故障診斷[J];軸承;2009年07期

3 魏云冰,黃進(jìn),牛發(fā)亮,任秉知;基于小波脊線的籠型異步電動(dòng)機(jī)轉(zhuǎn)子故障特征提取[J];電工技術(shù)學(xué)報(bào);2003年04期

4 高靜懷,汪文秉,朱光明;小波變換與信號(hào)瞬時(shí)特征分析[J];地球物理學(xué)報(bào);1997年06期

5 楊宇,于德介,程軍圣,丁戈;經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)在滾動(dòng)軸承故障診斷中的應(yīng)用[J];湖南大學(xué)學(xué)報(bào)(自然科學(xué)版);2003年05期

6 張亢;程軍圣;楊宇;鄒憲軍;;基于分段波形的信號(hào)瞬時(shí)頻率計(jì)算方法[J];湖南大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年11期

7 陳家焱;陳章位;;液壓系統(tǒng)故障診斷技術(shù)的現(xiàn)狀與發(fā)展趨勢[J];機(jī)床與液壓;2008年10期

8 陸春月,王俊元;機(jī)械故障診斷的現(xiàn)狀與發(fā)展趨勢[J];機(jī)械管理開發(fā);2004年06期

9 郭艷平;顏文俊;;基于EMD和優(yōu)化K-均值聚類算法診斷滾動(dòng)軸承故障[J];計(jì)算機(jī)應(yīng)用研究;2012年07期

10 劉向東;包春江;;K-均值聚類法在機(jī)械設(shè)備鐵譜監(jiān)測技術(shù)中的應(yīng)用[J];機(jī)械設(shè)計(jì)與制造;2006年05期

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