發(fā)布時間：2018-12-26 12:40

【摘要】：隨著社會生產(chǎn)的發(fā)展，要求設(shè)備生產(chǎn)效率不斷提高。這就對液壓系統(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)時故障診斷就更加重要。軸向柱塞泵與滾動軸承故障信號是一種典型的非平穩(wěn)、非線性信號，這就需要一種適合處理非平穩(wěn)、非線性信號的方法準(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)可以把原始信號分解為一系列固有模態(tài)函數(shù)(IMF)之和。各個IMF分量起到了對數(shù)據(jù)特征放大的效果，對各個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，是由在時間—頻率面上滿足每個時刻點(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)合起來對故障信號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，本文對斜盤式軸向柱塞泵采集的振動信號與美國Case Western Reserve University軸承故障模擬試驗(yàn)臺采集的軸承故障振動信號進(jìn)行了分析研究。通過邊際譜對比提取故障信號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軸承各種狀態(tài)時的敏感頻率。并通過敏感IMF分量小波脊線解調(diào)后的時頻譜與Hilbert變換解調(diào)時頻譜進(jìn)行了對比，證明了該方法較Hilbert變換解調(diào)具有更高的時頻定位精度。 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，對降低采樣頻率后的包絡(luò)信號再次進(jìn)行EMD分解，利用再次分解后的IMF分量信號能量有效地提取了故障特征向量。利用K均值聚類方法對液壓泵與軸承的各種狀態(tài)進(jìn)行了故障模式識別，通過與Hilbert變換解調(diào)提取的特征向量對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)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TN911.7

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