基于支持向量機的轉子系統(tǒng)故障診斷方法研究
發(fā)布時間：2018-10-13 09:32
【摘要】：近年來,關于設備狀態(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,相關的理論研究也得到迅速發(fā)展。支持向量機在解決基于小樣本情況的分類問題方面表現出良好的性能。它根據結構風險最小化原則,具有全局最優(yōu)解,根據有限的樣本信息在模型的復雜性和學習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“過學習”問題。 本文結合轉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行特征提取。為了使支持向量機具有更高的分類準確率,運用粒子蟻群算法對支持向量機的參數進行優(yōu)化。針對故障多分類問題圍繞以上實驗分析和理論算法,本文的主要工作內容和研究結論如下: 1)在轉子實驗臺上模擬了四種典型故障,分析了四種故障的機理并對故障信號進行了濾波消澡、頻譜分析、軸心軌跡分析。在此基礎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的訓練樣本和測試樣本存在數據冗余問題,故以熵帶數據為基礎,對其作為SVM的訓練樣本進行了數據預處理研究。包括樣本歸一化和主元特征提取。后續(xù)的實驗表明,經過處理后的熵帶數據不僅能夠反映振動信號的特征,而且適合SVM進行模型訓練和故障分類。 3)以構造最優(yōu)分類器為目標,系統(tǒng)地研究了PSO算法和GA算法優(yōu)化SVM參數后對分類準確率的影響。通過把已經處理好的數據輸入到SVM中,分別應用GA和PSO對SVM的核參數與懲罰因子優(yōu)化并對未知故障類別的樣本測試發(fā)現,GA優(yōu)化后的SVM分類性能較差,且模型訓練時間較長,而PSO優(yōu)化得到的SVM具有良好的分類準確率和較快的訓練時間。 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用PSO算法進行參數尋優(yōu)。并基于以上算法流程開發(fā)了一套基于MATLAB GUI的轉子故障診斷系統(tǒng),子系統(tǒng)一可以實現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)二可以根據樣本特點對分類器進行參數尋優(yōu),實現對未知故障的判別,實驗結果驗證了該系統(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.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3
本文編號：2268118
[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.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3
【引證文獻】
相關碩士學位論文 前2條
1 許紅波;基于環(huán)境參數的過渡環(huán)境下人體熱感覺預測[D];大連理工大學;2011年
2 胡常安;基于混合雜草算法—神經網絡的轉子故障數據分類方法研究[D];蘭州理工大學;2012年
，本文編號：2268118
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