【摘要】：滾動軸承作為機械設(shè)備中一個重要的組成部分,對其進行狀態(tài)檢測和故障診斷具有很強的現(xiàn)實意義。本文利用希爾伯特-黃變換法(HHT)對滾動軸承故障信號進行能量特征值提取,進而利用支持向量機(SVM)的方法對滾動軸承故障狀態(tài)進行識別。 滾動軸承故障診斷主要包括診斷信息的獲取,故障特征值的提取和模式識別三個部分。其中故障特征的提取和狀態(tài)識別是滾動軸承故障診斷的關(guān)鍵。當(dāng)滾動軸承發(fā)生故障時,其振動信號往往表現(xiàn)為非平穩(wěn)性,本文提出的希爾伯特-黃變換法中的EMD分解法是基于信號的局部時間特征尺度,具有很強的自適應(yīng)性,可以將信號分解為有限個本征模函數(shù)(IMF)之和,每個IMF分量分別包括了不同時間特征尺度大小的成分,其尺度依次由小到大,因此,每個IMF分量包含了從高到低不同頻率段信號成分。本文將EMD方法引入滾動軸承故障診斷,選取故障信息明顯的IMF分量,提取出其能量特征向量,實現(xiàn)了滾動軸承故障的初步診斷。 在對滾動軸承進行模式識別上本文采用了支持向量機方法,因它具有對經(jīng)驗的依賴小,能夠獲得全局最優(yōu)解以及良好的泛化性能等特點,已被廣泛應(yīng)用于模式識別中。本文將提取到的IMF分量的能量特征向量作為支持向量機的輸入從而進行分類應(yīng)用于滾動軸承故障診斷識別中,實現(xiàn)了對滾動軸承故障狀態(tài)準確的診斷識別。
[Abstract]:As an important part of mechanical equipment, rolling bearing has strong practical significance in condition detection and fault diagnosis. In this paper, the Hilbert-Huang transform (HHT) method is used to extract the energy eigenvalues of the rolling bearing fault signal, and then the support vector machine (SVM) is used to identify the fault state of the rolling bearing. The fault diagnosis of rolling bearing consists of three parts: the acquisition of diagnosis information, the extraction of fault characteristic value and the pattern recognition. Fault feature extraction and state recognition are the key points of rolling bearing fault diagnosis. When the rolling bearing fails, the vibration signal is usually non-stationary. The EMD decomposition method proposed in this paper is based on the local time characteristic scale of the signal, and has strong self-adaptability. The signal can be decomposed into the sum of a finite number of eigenmode functions (IMF). Each IMF component includes components of different time characteristic scales, and their scales are small to large in turn, so each IMF component includes components of different time characteristic scales, so the scale of each IMF component is from small to large. Each IMF component contains signal components from high to low frequencies. In this paper, the EMD method is introduced into the fault diagnosis of rolling bearing, and the IMF component with obvious fault information is selected. The energy characteristic vector is extracted, and the primary diagnosis of rolling bearing fault is realized. In this paper, the support vector machine (SVM) method is used in the pattern recognition of rolling bearings. It has been widely used in pattern recognition because of its small dependence on experience, the ability to obtain the global optimal solution and the good generalization performance. In this paper, the energy eigenvector of the extracted IMF component is used as the input of the support vector machine and applied to the fault diagnosis and recognition of the rolling bearing, and the accurate diagnosis and recognition of the fault state of the rolling bearing is realized.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TP18

【引證文獻】

相關(guān)博士學(xué)位論文 前1條

1 胡勁松;面向旋轉(zhuǎn)機械故障診斷的經(jīng)驗?zāi)B(tài)分解時頻分析方法及實驗研究[D];浙江大學(xué);2003年

相關(guān)碩士學(xué)位論文 前3條

1 范超;旋轉(zhuǎn)機械振動故障信號微弱特征提取方法研究[D];東北石油大學(xué);2013年

2 莫嘉林;基于代價敏感布雷格曼散度的旋轉(zhuǎn)機械軸承故障診斷研究[D];長沙理工大學(xué);2013年

3 周亮;礦用絞車滾動軸承故障診斷系統(tǒng)設(shè)計[D];重慶大學(xué);2014年

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本文編號：2462532