本文選題：齒輪箱　切入點(diǎn)：希爾伯特-黃變換(HHT)　出處：《中北大學(xué)》2011年碩士論文

【摘要】：齒輪箱作為機(jī)械設(shè)備中一個(gè)重要的組成部分,對(duì)其進(jìn)行狀態(tài)檢測(cè)和故障診斷具有很強(qiáng)的現(xiàn)實(shí)意義。本文通過對(duì)齒輪箱常見故障進(jìn)行模擬實(shí)驗(yàn),利用希爾伯特變換法(HHT)對(duì)測(cè)得的故障信號(hào)進(jìn)行特征值提取,進(jìn)而利用支持向量機(jī)(SVM)的方法對(duì)齒輪箱故障狀態(tài)進(jìn)行識(shí)別,得到了較好的效果。 齒輪箱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(shí)別三個(gè)部分。其中故障特征的提取和狀態(tài)識(shí)別是齒輪箱故障診斷的關(guān)鍵。當(dāng)齒輪箱發(fā)生故障時(shí),其振動(dòng)信號(hào)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(hào)的局部時(shí)間特征尺度,具有很強(qiáng)的自適應(yīng)性,可以將信號(hào)分解為有限個(gè)內(nèi)稟模態(tài)函數(shù)(IMF)之和,每個(gè)IMF分量分別包括了不同時(shí)間特征尺度大小的成分,其尺度依次由小到大,因此,每個(gè)IMF分量包含了從高到低不同頻率段信號(hào)成分。本文將EMD方法引入齒輪箱故障診斷,選取故障信息明顯的IMF分量,進(jìn)而求得邊際譜圖,實(shí)現(xiàn)了齒輪箱故障初步診斷。 支持向量機(jī)方法是統(tǒng)計(jì)學(xué)習(xí)的一種,是在統(tǒng)計(jì)學(xué)習(xí)理論基礎(chǔ)上發(fā)展起來一種新的機(jī)器學(xué)習(xí)方法。目前,支持向量機(jī)已經(jīng)成為解決非線性分類問題的一種強(qiáng)有力的工具。它具有對(duì)經(jīng)驗(yàn)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(diǎn),已被廣泛應(yīng)用于模式識(shí)別中。本文將EMD能量特征提取法和支持向量機(jī)相結(jié)合應(yīng)用于齒輪箱故障診斷識(shí)別中,實(shí)現(xiàn)了對(duì)齒輪箱故障狀態(tài)準(zhǔn)確的診斷識(shí)別。
[Abstract]:As an important part of mechanical equipment, gearbox has a strong practical significance for condition detection and fault diagnosis.In this paper, through simulating the common faults of the gearbox, using Hilbert transform (HHT) to extract the eigenvalue of the measured fault signal, and then using the support vector machine (SVM) method to identify the fault state of the gearbox.Good results were obtained.Gearbox fault diagnosis includes three parts: obtaining diagnosis information, extracting fault eigenvalue and pattern recognition.Fault feature extraction and state recognition are the key of gearbox fault diagnosis.When the gearbox fails, the vibration signal is usually non-stationary. The EMD decomposition method of Hilbert-Huang transform is based on the local time characteristic scale of the signal and has strong adaptability.The signal can be decomposed into the sum of a finite intrinsic mode function. Each IMF component consists of components of different temporal characteristic scales, whose scales range from small to large, so,Each IMF component contains signal components at different frequencies from high to low.In this paper, the EMD method is introduced into the gearbox fault diagnosis, and the obvious IMF component of the fault information is selected, then the marginal spectrum is obtained, and the primary diagnosis of the gearbox fault is realized.Support vector machine (SVM) is a new machine learning method based on statistical learning theory.At present, support vector machine (SVM) has become a powerful tool to solve nonlinear classification problem.It has been widely used in pattern recognition due to its small dependence on experience, its ability to obtain global optimal solutions and its good generalization performance.In this paper, the EMD energy feature extraction method and support vector machine are combined in the gearbox fault diagnosis and identification, and the accurate diagnosis and recognition of the gearbox fault state is realized.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 李桃;基于粒子濾波技術(shù)的齒輪箱故障診斷研究[D];中北大學(xué);2012年

2 劉芽;基于EEMD和支持向量機(jī)的刀具狀態(tài)監(jiān)測(cè)方法研究[D];西南交通大學(xué);2012年

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本文編號(hào)：1717473