本文關(guān)鍵詞：基于改進(jìn)BFA的旋轉(zhuǎn)機(jī)械故障診斷核參數(shù)優(yōu)選研究　出處：《湖南科技大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 旋轉(zhuǎn)機(jī)械 故障診斷 核主元分析 支持向量機(jī) 參數(shù)優(yōu)化 細(xì)菌覓食算法

【摘要】：旋轉(zhuǎn)機(jī)械是現(xiàn)代工業(yè)生產(chǎn)中的核心設(shè)備,開展旋轉(zhuǎn)機(jī)械故障診斷技術(shù)研究,對于確保此類設(shè)備安全、高效運行,避免巨大的經(jīng)濟(jì)損失和災(zāi)難性事故的發(fā)生,具有極大的經(jīng)濟(jì)、社會意義,同時也是對機(jī)械設(shè)備狀態(tài)監(jiān)測與故障診斷技術(shù)的豐富與發(fā)展。 論文以旋轉(zhuǎn)機(jī)械為對象,開展基于典型核方法——核主元分析(Kernel Principal Component Analysis,KPCA)和支持向量機(jī)(Support Vector Machine,SVM)的故障診斷方法與技術(shù)研究,針對KPCA、SVM性能受核函數(shù)及其參數(shù)影響很大,而最優(yōu)參數(shù)難以選取的問題,論文提出了一種改進(jìn)的細(xì)菌覓食算法(Bacterial Foraging Algorithm,BFA),展開旋轉(zhuǎn)機(jī)械故障診斷中核參數(shù)優(yōu)化選取研究。主要研究內(nèi)容如下: 1、分析了標(biāo)準(zhǔn)細(xì)菌覓食算法中存在的問題,如種群的大小、運動步長、其迭代次數(shù)完全由各種操作所設(shè)定的最大次數(shù)決定、沒有引入收斂準(zhǔn)則,難以保證求解的精度并增加不必要的迭代過程,提出了一種改進(jìn)細(xì)菌覓食算法,二維連續(xù)函數(shù)仿真實驗證明了改進(jìn)后的細(xì)菌覓食算法不僅提高了優(yōu)化速度,而且提高了求解的精度。 2、分析了核參數(shù)對KPCA特征降維的影響,設(shè)計了基于改進(jìn)細(xì)菌覓食算法的KPCA特征提取算法,旋轉(zhuǎn)機(jī)械故障特征實例表明,該方法能夠快速、準(zhǔn)確的對KPCA核參數(shù)進(jìn)行優(yōu)化。 3、設(shè)計了基于改進(jìn)細(xì)菌覓食算法的SVM參數(shù)優(yōu)化算法,分析和比較改進(jìn)后的細(xì)菌覓食算法與傳統(tǒng)的優(yōu)化算法包括遺傳算法、粒子群算法和交叉驗證法之間的尋優(yōu)性能,結(jié)果表明改進(jìn)后的細(xì)菌覓食算法優(yōu)與其他算法。 4、將基于改進(jìn)細(xì)菌覓食算法的KPCA和基于改進(jìn)細(xì)菌覓食算法的SVM應(yīng)用到旋轉(zhuǎn)機(jī)械故障診斷中,實現(xiàn)了基于基座多傳感信息融合的滾動軸承故障診斷和基于多傳感器信息融合的齒輪故障診斷。實驗結(jié)果證明了本文所提方法的優(yōu)越性,同時,實驗中基于基座的傳感器安裝方式可克服現(xiàn)場傳感器安裝不便等問題,為故障診斷中振動測試提供了一種有用的參考方案,具有極大的應(yīng)用推廣前景。
[Abstract]:Rotating machinery is the key equipment in modern industrial production, to carry out the research of fault diagnosis of rotating machinery, to ensure the equipment safety, efficient operation, to avoid huge economic losses and catastrophic accidents, has great economic and social significance, but also for the machinery and equipment condition monitoring and fault diagnosis technology for rich and development.
This paper takes the rotating machinery as the objects, carry out based on typical kernel methods - kernel principal component analysis (Kernel Principal Component Analysis, KPCA) and support vector machine (Support Vector Machine, SVM), and the technology of fault diagnosis method based on KPCA, the performance of SVM is much influenced by kernel function and its parameters, and the optimal parameters are difficult to select problems, this thesis proposes an improved bacterial foraging algorithm (Bacterial Foraging Algorithm, BFA), launched a selection of optimization of rotating machinery fault diagnosis of nuclear parameters. The main research contents are as follows:
1, analysis of the standard bacterial foraging algorithm in the existing problems, such as population size, movement step length, maximum number of iterations by various operations set by the decision, without introducing the convergence criteria, it is difficult to guarantee the accuracy and increase the unnecessary iterative process, and proposes an improved bacterial foraging algorithm, two-dimensional continuous the function simulation experiment proves that the improved bacterial foraging algorithm not only improves the optimization speed, but also improve the accuracy of solution.
2, the influence of nuclear parameters on KPCA feature reduction is analyzed. A KPCA feature extraction algorithm based on improved bacterial foraging algorithm is designed. The example of rotating machinery fault feature shows that this method can optimize KPCA kernel parameters quickly and accurately.
3, the design of SVM parameter optimization algorithm of the improved bacterial foraging algorithm based on the analysis and comparison of the improved bacterial foraging optimization algorithm and traditional algorithm including genetic algorithm, particle swarm algorithm and cross validation between the optimization performance, the results show that the improved bacterial foraging algorithm and other algorithms.
4, the improved bacterial foraging algorithm KPCA and SVM using the improved bacterial foraging algorithm based on fault diagnosis of rotating machinery based on the gear fault diagnosis of rolling bearing fault diagnosis base based on multi sensor information fusion and based on multi-sensor information fusion. Experimental results prove the superiority of the proposed method in this paper. At the same time. The sensor installation base, based on the experiment can overcome the inconvenience of installation of field sensor, provides a useful reference scheme for fault diagnosis of vibration test, and has a great application prospect.

【學(xué)位授予單位】：湖南科技大學(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1條

1 王海娟;考慮葉輪前側(cè)蓋板流固耦合的轉(zhuǎn)子系統(tǒng)動力學(xué)特性研究[D];鄭州大學(xué);2012年

，

本文編號：1368284