【摘要】：近年來,關(guān)于設(shè)備狀態(tài)監(jiān)測與故障診斷方面的研究工作得到越來越高的重視,相關(guān)的理論研究也得到迅速發(fā)展。支持向量機(jī)在解決基于小樣本情況的分類問題方面表現(xiàn)出良好的性能。它根據(jù)結(jié)構(gòu)風(fēng)險最小化原則,具有全局最優(yōu)解,根據(jù)有限的樣本信息在模型的復(fù)雜性和學(xué)習(xí)能之間尋求最佳折衷,以獲得最好的推廣能力并能有效地解決“過學(xué)習(xí)”問題。 本文結(jié)合轉(zhuǎn)子實驗臺上模擬的常見故障,采用熵帶法對故障振動信號進(jìn)行特征提取。為了使支持向量機(jī)具有更高的分類準(zhǔn)確率,運(yùn)用粒子蟻群算法對支持向量機(jī)的參數(shù)進(jìn)行優(yōu)化。針對故障多分類問題圍繞以上實驗分析和理論算法,本文的主要工作內(nèi)容和研究結(jié)論如下: 1)在轉(zhuǎn)子實驗臺上模擬了四種典型故障,分析了四種故障的機(jī)理并對故障信號進(jìn)行了濾波消澡、頻譜分析、軸心軌跡分析。在此基礎(chǔ)上分析了信號在時域的奇異值譜熵、頻域的功率譜熵、時頻域的小波能譜熵和小波空間譜熵。并計算了四種故障信號的熵帶范圍,討論了常規(guī)的基于信息熵的故障診斷方法。 2)因直接把熵帶作為SVM的訓(xùn)練樣本和測試樣本存在數(shù)據(jù)冗余問題,故以熵帶數(shù)據(jù)為基礎(chǔ),對其作為SVM的訓(xùn)練樣本進(jìn)行了數(shù)據(jù)預(yù)處理研究。包括樣本歸一化和主元特征提取。后續(xù)的實驗表明,經(jīng)過處理后的熵帶數(shù)據(jù)不僅能夠反映振動信號的特征,而且適合SVM進(jìn)行模型訓(xùn)練和故障分類。 3)以構(gòu)造最優(yōu)分類器為目標(biāo),系統(tǒng)地研究了PSO算法和GA算法優(yōu)化SVM參數(shù)后對分類準(zhǔn)確率的影響。通過把已經(jīng)處理好的數(shù)據(jù)輸入到SVM中,分別應(yīng)用GA和PSO對SVM的核參數(shù)與懲罰因子優(yōu)化并對未知故障類別的樣本測試發(fā)現(xiàn),GA優(yōu)化后的SVM分類性能較差,且模型訓(xùn)練時間較長,而PSO優(yōu)化得到的SVM具有良好的分類準(zhǔn)確率和較快的訓(xùn)練時間。 4)由于本研究是多故障分類問題,而SVM是二分類器,故基于一對多的方法設(shè)計了可以分離四種故障的SVM多故障分類器。對各個分類器分別應(yīng)用PSO算法進(jìn)行參數(shù)尋優(yōu)。并基于以上算法流程開發(fā)了一套基于MATLAB GUI的轉(zhuǎn)子故障診斷系統(tǒng),子系統(tǒng)一可以實現(xiàn)對振動信號的消澡分析,頻譜分析,軸心軌跡分析等;子系統(tǒng)二可以根據(jù)樣本特點對分類器進(jìn)行參數(shù)尋優(yōu),實現(xiàn)對未知故障的判別,實驗結(jié)果驗證了該系統(tǒng)的有效性。
[Abstract]:In recent years, more and more attention has been paid to the research of equipment condition monitoring and fault diagnosis, and the related theoretical research has been developed rapidly. Support vector machines (SVM) show good performance in solving classification problems based on small samples. According to the principle of structural risk minimization, it has the global optimal solution. According to the limited sample information, it seeks the best tradeoff between the complexity of the model and the learning ability in order to obtain the best generalization ability and solve the "overlearning" problem effectively. In this paper, the entropy band method is used to extract the characteristic of the fault vibration signal in combination with the common faults simulated on the rotor test bench. In order to make SVM have higher classification accuracy, particle ant colony algorithm is used to optimize the parameters of SVM. The main contents and conclusions of this paper are as follows: 1) four kinds of typical faults are simulated on the rotor test bench. The mechanism of four kinds of faults is analyzed, and the fault signals are analyzed by filtering bath, spectrum analysis and axis locus analysis. The singular value spectral entropy in time domain, power spectrum entropy in frequency domain, wavelet spectrum entropy and wavelet space spectral entropy in time-frequency domain are analyzed. The entropy band range of four kinds of fault signals is calculated, and the conventional fault diagnosis method based on information entropy is discussed. 2) there is data redundancy in using entropy band directly as SVM training sample and test sample. Therefore, based on the entropy band data, the data preprocessing for SVM training samples is studied. It includes sample normalization and principal component feature extraction. The subsequent experiments show that the processed entropy band data can not only reflect the characteristics of vibration signals, but also be suitable for SVM model training and fault classification. 3) the goal of constructing an optimal classifier is to construct an optimal classifier. The influence of PSO algorithm and GA algorithm on the classification accuracy is studied systematically after optimizing the SVM parameters. By inputting the processed data into SVM, applying GA and PSO to optimize the kernel parameters and penalty factors of SVM, and testing the samples of unknown fault categories, it is found that the SVM classification performance after GA optimization is poor, and the model training time is longer. However, the SVM optimized by PSO has good classification accuracy and fast training time. 4) because this study is a multi-fault classification problem, SVM is a two-classifier. Therefore, based on one-to-many method, SVM multi-fault classifier which can separate four kinds of faults is designed. The PSO algorithm is used to optimize the parameters of each classifier. Based on the above algorithm flow, a rotor fault diagnosis system based on MATLAB GUI is developed. Subsystem one can realize vibration signal analysis, spectrum analysis, axis trajectory analysis and so on. The second subsystem can optimize the classifier parameters according to the characteristics of the samples and realize the identification of unknown faults. The experimental results show that the system is effective.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 許紅波;基于環(huán)境參數(shù)的過渡環(huán)境下人體熱感覺預(yù)測[D];大連理工大學(xué);2011年

2 胡常安;基于混合雜草算法—神經(jīng)網(wǎng)絡(luò)的轉(zhuǎn)子故障數(shù)據(jù)分類方法研究[D];蘭州理工大學(xué);2012年

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本文編號：2268118