【摘要】：在設(shè)備故障診斷中,傳統(tǒng)的數(shù)據(jù)處理方法只能針對(duì)單通道數(shù)據(jù)進(jìn)行分析,而單通道數(shù)據(jù)往往不能將設(shè)備的空間運(yùn)動(dòng)信息完整的表征出來。而作為全信息分析方法的一種,全矢譜分析技術(shù)在處理同源多通道故障信號(hào)的同時(shí)能夠體現(xiàn)更全面準(zhǔn)確的轉(zhuǎn)子運(yùn)動(dòng)空間特征信息。在此基礎(chǔ)上本文將全矢譜技術(shù)與支持向量數(shù)據(jù)描述相結(jié)合,提出了全矢譜支持向量數(shù)據(jù)描述(Vector Spectrum Support Vector Data Description, VSSVDD)故障診斷方法。針對(duì)支持向量數(shù)據(jù)描述(Support Vector Data Description, SVDD)分類方法中訓(xùn)練樣本數(shù)目受限的問題,本文對(duì)SVDD分類器作二次改進(jìn),引入動(dòng)態(tài)支持向量數(shù)據(jù)描述(DSVDD)分類方法。該方法在訓(xùn)練樣本中不斷注入新的樣本進(jìn)而不斷更新分類邊界,從而更準(zhǔn)確的表征了目標(biāo)樣本的區(qū)域邊界。本文主要研究和解決問題如下： 第一,支持向量數(shù)據(jù)描述方法是建立在統(tǒng)計(jì)學(xué)習(xí)理論之上的,核函數(shù)的引入可以把低維空間的非線性問題轉(zhuǎn)化為高維空間的線性問題。選擇不同的核函數(shù)對(duì)SVDD的分類效果不同。 第二,運(yùn)用全矢譜分析方法對(duì)采樣數(shù)據(jù)進(jìn)行分析處理,并且提取典型倍頻上的幅值作為SVDD分類器的特征向量。實(shí)驗(yàn)表明經(jīng)過全矢譜特征提取后的SVDD的分類效果較未經(jīng)特征提取SVDD的分類效果更為明顯。通過實(shí)驗(yàn)研究驗(yàn)證了全矢譜支持向量數(shù)據(jù)描述故障診斷方法對(duì)測(cè)試樣本進(jìn)行分類的可行性與有效性。 第三,運(yùn)用全矢譜支持向量數(shù)據(jù)描述方法對(duì)設(shè)備性能退化評(píng)估引入隸屬度和相對(duì)距離的概念避免了超球體邊界誤差帶來的影響,可以將測(cè)試樣本的狀態(tài)更加精確的表述出來；同時(shí)又體現(xiàn)了狀態(tài)變化的過程。 第四,提出動(dòng)態(tài)支持向量數(shù)據(jù)描述分類方法的改進(jìn)型。該方法的提出改變了原來SVDD分類方法中,分類器經(jīng)過一次訓(xùn)之后分類邊界永不改變的現(xiàn)狀。它將測(cè)試得到的目標(biāo)樣本與本次測(cè)試以前的支持向量集一起形成新的訓(xùn)練樣本,然后對(duì)SVDD重新訓(xùn)練。這樣分類邊界將更能體現(xiàn)設(shè)備的正常樣本特征。
[Abstract]:In the fault diagnosis of equipment, the traditional data processing method can only analyze the single channel data, but the single channel data can not completely represent the spatial motion information of the equipment. As one of the methods of full information analysis, the full vector spectrum analysis technique can not only deal with the same source multi-channel fault signal, but also reflect the more comprehensive and accurate information of the rotor motion space. On this basis, this paper combines full vector spectrum technique with support vector data description, and proposes a fault diagnosis method for (Vector Spectrum Support Vector Data Description, VSSVDD) based on full vector spectrum support vector data description. In order to solve the problem that the number of training samples is limited in the support vector data description (Support Vector Data Description, SVDD) classification method, this paper makes a second improvement on the SVDD classifier, and introduces the dynamic support vector data description (DSVDD) classification method. In this method, new samples are continuously injected into the training samples, and then the classification boundaries are constantly updated, thus representing the region boundary of the target samples more accurately. In this paper, the main research and solutions are as follows: first, support vector data description method is based on statistical learning theory, the introduction of kernel function can transform the nonlinear problem of low-dimensional space into the linear problem of high-dimensional space. Different kernel functions have different effects on SVDD classification. Secondly, the method of full vector spectrum analysis is used to analyze and process the sampled data, and the amplitude on the typical frequency doubling is extracted as the feature vector of the SVDD classifier. The experimental results show that the classification effect of SVDD after full vector feature extraction is more obvious than that of SVDD without feature extraction. The feasibility and effectiveness of the fault diagnosis method based on full vector spectrum support vector data description for classification of test samples is verified by experimental research. Thirdly, the concept of membership degree and relative distance is introduced into the evaluation of equipment performance degradation by using full vector spectral support vector data description method to avoid the influence of hypersphere boundary error, and the state of test samples can be expressed more accurately. At the same time, it reflects the process of state change. Fourth, an improved classification method for dynamic support vector data description is proposed. The proposed method changes the status quo of the original SVDD classification method, in which the classification boundary of the classifier never changes after a training. It forms a new training sample with the support vector set before this test, and then retrains the SVDD. In this way, the classification boundary can better reflect the normal sample characteristics of the equipment.
【學(xué)位授予單位】：鄭州大學(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1條

1 付玉榮;全矢—模糊聚類及其在故障診斷中的應(yīng)用研究[D];鄭州大學(xué);2013年

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本文編號(hào)：2471739