本文關鍵詞：基于支持向量機和免疫算法的故障檢測與診斷　出處：《華東理工大學》2011年碩士論文　論文類型：學位論文

  更多相關文章： 支持向量機 故障診斷 免疫遺傳算法 克隆選擇算法 TE過程

【摘要】：隨著現(xiàn)代技術的不斷發(fā)展,工業(yè)規(guī)模不斷擴大,生產(chǎn)設備也越來越復雜,工業(yè)生產(chǎn)過程中的安全性越來越受到人們的重視,因此過程監(jiān)控與故障診斷成為近年來的研究熱點。近年來,支持向量機作為一種新型的機器學習方法得到廣泛的應用,并且在小樣本的情況下表現(xiàn)出其優(yōu)勢。本文考慮到工業(yè)過程系統(tǒng)復雜等特點,對支持向量機和免疫算法在工業(yè)過程的故障診斷中的應用進行了深入研究。 本文對工業(yè)過程中的故障檢測和診斷方法進行了詳細的綜述,比較了各種檢測及診斷方法的性能,介紹了統(tǒng)計學習理論和用于分類的支持向量機的基本原理,研究了支持向量機核函數(shù)參數(shù)對故障檢測及診斷效果的影響。為了改善支持向量機故障診斷的性能,提出了將免疫算法與支持向量機相結合的故障檢測及診斷算法,分別對免疫遺傳算法和克隆選擇算法進行改進,提出了基于改進免疫遺傳算法的支持向量機和基于改進克隆選擇算法的支持向量機,并將其應用于故障檢測和診斷。為了驗證所提出的方法的有效性,以標準仿真模型TE模型為平臺,將其用于TE過程的故障診斷。仿真結果表明,本文提出的基于改進免疫遺傳算法的支持向量機和基于改進克隆選擇算法的支持向量機具有較高的故障診斷效率。
[Abstract]:With the development of modern technology, the scale of industry is expanding, and the production equipment is becoming more and more complex. People pay more and more attention to the safety in the process of industrial production. Therefore, process monitoring and fault diagnosis have become the focus of research in recent years. In recent years, support vector machine as a new machine learning method has been widely used. Considering the complexity of industrial process system, the application of support vector machine and immune algorithm in fault diagnosis of industrial process is deeply studied. In this paper, the methods of fault detection and diagnosis in industrial process are reviewed in detail, the performance of various detection and diagnosis methods are compared, and the statistical learning theory and the basic principle of support vector machine for classification are introduced. In order to improve the performance of SVM fault diagnosis, the effect of kernel function parameters on fault detection and diagnosis is studied. A fault detection and diagnosis algorithm combining immune algorithm and support vector machine is proposed, which improves immune genetic algorithm and clonal selection algorithm respectively. Support vector machine based on improved immune genetic algorithm and support vector machine based on improved clonal selection algorithm are proposed and applied to fault detection and diagnosis. Based on the standard simulation model te model, it is used in the fault diagnosis of te process. The simulation results show that. The proposed support vector machine based on improved immune genetic algorithm and support vector machine based on improved clonal selection algorithm have high fault diagnosis efficiency.
【學位授予單位】：華東理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3;TP18

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本文編號：1415107