本文選題：支持向量機(jī) + 故障診斷��； 參考：《電子科技大學(xué)》2011年碩士論文

【摘要】：支持向量機(jī)是一種在統(tǒng)計(jì)學(xué)習(xí)理論基礎(chǔ)上發(fā)展起來(lái)的新型的機(jī)器學(xué)習(xí)方法。在解決小樣本問(wèn)題、高維問(wèn)題和局部極值的問(wèn)題時(shí)表現(xiàn)出了十分優(yōu)良的特性,另外,支持向量機(jī)還具有十分簡(jiǎn)單的結(jié)構(gòu),這兩點(diǎn)決定了支持向量機(jī)在人工智能領(lǐng)域的特殊性,且適于應(yīng)用在故障診斷領(lǐng)域。 由于支持向量機(jī)的訓(xùn)練樣本具有冗余性,有些訓(xùn)練樣本距離分類面很遠(yuǎn),并且還不會(huì)對(duì)分類結(jié)果產(chǎn)生很大的影響,這些樣本是可以剔除的。另外,支持向量機(jī)的參數(shù)選擇對(duì)分類結(jié)果也會(huì)有影響,因此本文以減少支持向量機(jī)訓(xùn)練時(shí)間,提高分類器準(zhǔn)確率為出發(fā)點(diǎn),對(duì)支持向量機(jī)訓(xùn)練樣本的預(yù)處理和參數(shù)優(yōu)化進(jìn)行了研究和改進(jìn)。 故障診斷問(wèn)題是十分復(fù)雜的,故障診斷技術(shù)的發(fā)展是向著智能信息處理技術(shù)前進(jìn)的。本文主要是研究基于模糊C均值的支持向量機(jī)訓(xùn)練樣本預(yù)處理及粒子群參數(shù)優(yōu)化的支持向量機(jī)的故障診斷方法,探索新的、更高效、精度更高的故障診斷分類方法。 本文首先為了對(duì)支持向量機(jī)的冗余訓(xùn)練樣本進(jìn)行預(yù)處理,研究了基于模糊C均值算法的支持向量機(jī)的實(shí)現(xiàn),并通過(guò)數(shù)值試驗(yàn)來(lái)驗(yàn)證了模糊C均值算法對(duì)支持向量機(jī)訓(xùn)練樣本的處理效果,結(jié)果表明,在分類準(zhǔn)確率相差不大的情況下,使用模糊C均值算法對(duì)支持向量機(jī)的訓(xùn)練樣本進(jìn)行預(yù)處理的分類時(shí)間被大大縮短了。接著研究了基于粒子群算法的最小二乘支持向量機(jī)的參數(shù)優(yōu)化問(wèn)題,給出了算法的步驟并進(jìn)行了數(shù)值驗(yàn)證,得出采用粒子群算法對(duì)參數(shù)進(jìn)行優(yōu)化可以提高分類準(zhǔn)確率的結(jié)論。在分別驗(yàn)證了使用這兩種方法對(duì)算法的改進(jìn)作用之后,最后將兩者結(jié)合,采用基于模糊C均值和粒子群參數(shù)優(yōu)化的最小二乘支持向量機(jī)來(lái)對(duì)滾動(dòng)軸承故障數(shù)據(jù)進(jìn)行了分類測(cè)試,通過(guò)與傳統(tǒng)向量機(jī)、使用上述兩種方法改進(jìn)的支持向量機(jī)的比較,本文所提出的算法在保證較高的分類準(zhǔn)確率的情況下,可以有效地減少訓(xùn)練時(shí)間,提高分類效率。
[Abstract]:Support vector machine (SVM) is a new machine learning method developed on the basis of statistical learning theory. In solving the problem of small sample, high dimension and local extremum, support vector machine (SVM) has a very simple structure, which determines the particularity of support vector machine (SVM) in the field of artificial intelligence. It is suitable for fault diagnosis. Because the training samples of SVM are redundant, some of the training samples are far from the classification surface and will not have a great influence on the classification results. These samples can be eliminated. In addition, the selection of support vector machine parameters will also have an impact on the classification results, so this paper takes reducing the training time of support vector machine and improving the accuracy of classifier as the starting point. The pretreatment and parameter optimization of SVM training samples are studied and improved. The problem of fault diagnosis is very complex, and the development of fault diagnosis technology is advancing towards intelligent information processing technology. In this paper, the fault diagnosis method of SVM based on fuzzy C-means training sample preprocessing and particle swarm optimization is studied, and a new, more efficient and accurate fault diagnosis classification method is explored. Firstly, in order to preprocess the redundant training samples of SVM, the realization of SVM based on fuzzy C-means algorithm is studied. The numerical results show that the fuzzy C-means algorithm can deal with the training samples of SVM. The results show that the classification accuracy is not different from each other. The classification time of training samples of support vector machine (SVM) is greatly reduced by using fuzzy C-means algorithm. Then the parameter optimization problem of least squares support vector machine based on particle swarm optimization algorithm is studied. The steps of the algorithm are given and the numerical results are given. The conclusion is drawn that the particle swarm optimization algorithm can improve the classification accuracy. After the improvement of the algorithm is verified by the two methods, the least squares support vector machine based on fuzzy C-means and particle swarm optimization is used to classify and test the rolling bearing fault data. Compared with the traditional vector machine, the proposed algorithm can effectively reduce the training time and improve the classification efficiency by using the improved support vector machine.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TP18;TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 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)：2053104