本文選題：譜聚類　切入點(diǎn)：密度　出處：《華南理工大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

【摘要】：由于機(jī)械(如汽車變速器)早期故障的特征信號(hào)微弱,容易淹沒在強(qiáng)噪聲之中,而且已知故障模式樣本不足,傳統(tǒng)的頻譜分析方法對(duì)故障的早期檢測(cè)不敏感,因此,開展早期故障智能預(yù)示的研究工作具有重要意義。 本文提出了基于密度可調(diào)譜聚類的半監(jiān)督SVM(DSTSVM)方法,利用基于密度可調(diào)譜聚類的思想對(duì)數(shù)據(jù)進(jìn)行特征提取,并且構(gòu)造半監(jiān)督SVM(TSVM)的核函數(shù),采用梯度下降法對(duì)TSVM進(jìn)行協(xié)同訓(xùn)練,實(shí)現(xiàn)對(duì)數(shù)據(jù)的分類,通過仿真和實(shí)例,將該方法與SVM、TSVM和基于聚類核的半監(jiān)督SVM(CKSVM)進(jìn)行對(duì)比分析,證明該方法能有效反映數(shù)據(jù)結(jié)構(gòu)信息,用少量已知標(biāo)簽樣本便能得到較高分類正確率。 利用傳動(dòng)試驗(yàn)臺(tái)對(duì)汽車變速箱進(jìn)行無故障、齒輪輕微點(diǎn)蝕和齒輪輕微剝落試驗(yàn),通過時(shí)域、頻域方法分析出早期故障診斷的困難所在,將基于密度可調(diào)譜聚類的半監(jiān)督SVM方法應(yīng)用到齒輪早期故障預(yù)示中,分別采用經(jīng)過PCA選擇的時(shí)域特征指標(biāo)、構(gòu)造的頻域能量因子作為輸入,并將多傳感器數(shù)據(jù)進(jìn)行融合學(xué)習(xí),與其它方法進(jìn)行對(duì)比,證明了該方法在齒輪故障預(yù)示中的有效性和優(yōu)越性。 采用美國西儲(chǔ)大學(xué)的電機(jī)軸承故障數(shù)據(jù),對(duì)內(nèi)圈、外圈、滾動(dòng)體故障做了時(shí)頻域分析,分析出滾動(dòng)體早期故障診斷的困難,采用SVM、TSVM、CKSVM和DSTSVM對(duì)滾動(dòng)體故障進(jìn)行檢測(cè),并且對(duì)四種模式進(jìn)行了分類識(shí)別,驗(yàn)證了DSTSVM方法在軸承早期故障預(yù)示中的有效性。
[Abstract]:Because the characteristic signal of early fault of machinery (such as automobile transmission) is weak, easily submerged in strong noise, and the sample of known fault mode is insufficient, the traditional spectrum analysis method is not sensitive to the early detection of fault. It is of great significance to carry out the research on early fault intelligent prediction. In this paper, a semi-supervised SVM DST SVM method based on density tunable spectral clustering is proposed. The feature extraction of data based on density tunable spectrum clustering is used, and the kernel function of semi-supervised SVMtSVM is constructed, and the gradient descent method is used to train TSVM cooperatively. The classification of data is realized. Through simulation and example, the method is compared with SVMN TSVM and semi-supervised SVMN CKSVM based on clustering kernel. It is proved that this method can effectively reflect the information of data structure. A high classification accuracy can be obtained by using a small number of known tag samples. The transmission test bench is used to test the automobile gearbox without fault, the gears are slightly pitting and the gears are peeling off slightly. The difficulties of early fault diagnosis are analyzed by time-domain and frequency-domain methods. The semi-supervised SVM method based on density adjustable spectrum clustering is applied to the early fault prediction of gears. The time-domain characteristic index selected by PCA is used to construct the frequency-domain energy factor as input, and the multi-sensor data is fused to learn. Compared with other methods, this method is proved to be effective and superior in gear fault prediction. Based on the fault data of motor bearing from the University of Western Reserve of USA, the fault of inner ring, outer ring and rolling body is analyzed in time and frequency domain, and the difficulty of early fault diagnosis of rolling body is analyzed. The fault of rolling body is detected by SVM TSVM CKSVM and DSTSVM. The classification and recognition of four kinds of patterns are carried out to verify the effectiveness of DSTSVM method in early bearing fault prediction.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王玲;薄列峰;焦李成;;密度敏感的譜聚類[J];電子學(xué)報(bào);2007年08期

2 鐘振茂,陳進(jìn),鐘平;盲源分離技術(shù)用于機(jī)械故障診斷的研究初探[J];機(jī)械科學(xué)與技術(shù);2002年02期

3 桂衛(wèi)華,劉曉穎;基于人工智能方法的復(fù)雜過程故障診斷技術(shù)[J];控制工程;2002年04期

4 張斌,張薇薇;機(jī)械設(shè)備故障診斷技術(shù)概述[J];建筑機(jī)械化;2005年08期

5 張學(xué)工;關(guān)于統(tǒng)計(jì)學(xué)習(xí)理論與支持向量機(jī)[J];自動(dòng)化學(xué)報(bào);2000年01期

6 程軍圣;于德介;楊宇;;基于內(nèi)稟模態(tài)奇異值分解和支持向量機(jī)的故障診斷方法[J];自動(dòng)化學(xué)報(bào);2006年03期

7 萬軍,蔣世祥,蔡智勇;旋轉(zhuǎn)機(jī)械振動(dòng)信號(hào)的小波包分解及故障檢測(cè)[J];汽輪機(jī)技術(shù);2002年02期

8 李凌均,張周鎖,何正嘉;基于支持向量機(jī)的機(jī)械故障智能分類研究[J];小型微型計(jì)算機(jī)系統(tǒng);2004年04期

9 鐘佑明,秦樹人,湯寶平;Hilbert-Huang變換中的理論研究[J];振動(dòng)與沖擊;2002年04期

10 屈梁生,史東鋒;全息譜十年:回顧與展望[J];振動(dòng).測(cè)試與診斷;1998年04期

相關(guān)碩士學(xué)位論文 前1條

1 朱元佳;汽車變速箱在線快速故障診斷技術(shù)研究[D];同濟(jì)大學(xué);2007年

，

本文編號(hào)：1651825