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

【摘要】：由于機械(如汽車變速器)早期故障的特征信號微弱,容易淹沒在強噪聲之中,而且已知故障模式樣本不足,傳統(tǒng)的頻譜分析方法對故障的早期檢測不敏感,因此,開展早期故障智能預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)譜聚類的思想對數(shù)據(jù)進行特征提取,并且構造半監(jiān)督SVM(TSVM)的核函數(shù),采用梯度下降法對TSVM進行協(xié)同訓練,實現(xiàn)對數(shù)據(jù)的分類,通過仿真和實例,將該方法與SVM、TSVM和基于聚類核的半監(jiān)督SVM(CKSVM)進行對比分析,證明該方法能有效反映數(shù)據(jù)結構信息,用少量已知標簽樣本便能得到較高分類正確率。 利用傳動試驗臺對汽車變速箱進行無故障、齒輪輕微點蝕和齒輪輕微剝落試驗,通過時域、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方法應用到齒輪早期故障預示中,分別采用經(jīng)過PCA選擇的時域特征指標、構造的頻域能量因子作為輸入,并將多傳感器數(shù)據(jù)進行融合學習,與其它方法進行對比,證明了該方法在齒輪故障預示中的有效性和優(yōu)越性。 采用美國西儲大學的電機軸承故障數(shù)據(jù),對內(nèi)圈、外圈、滾動體故障做了時頻域分析,分析出滾動體早期故障診斷的困難,采用SVM、TSVM、CKSVM和DSTSVM對滾動體故障進行檢測,并且對四種模式進行了分類識別,驗證了DSTSVM方法在軸承早期故障預示中的有效性。
[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.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

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