本文關(guān)鍵詞：基于PCA與蟻群算法的旋轉(zhuǎn)機(jī)械故障診斷方法研究　出處：《湖南科技大學(xué)》2012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 主元分析 蟻群算法 故障診斷

【摘要】：旋轉(zhuǎn)機(jī)械設(shè)備被廣泛應(yīng)用于現(xiàn)代工業(yè)中，這些設(shè)備一旦出現(xiàn)故障將會(huì)帶來(lái)巨大的經(jīng)濟(jì)損失。隨著計(jì)算機(jī)技術(shù)的高速發(fā)展，旋轉(zhuǎn)機(jī)械智能化、集成化程度越來(lái)越高，其出現(xiàn)的故障種類和形式越來(lái)越多，診斷時(shí)需要考慮的故障特征信息量也越來(lái)越大。由于大型復(fù)雜設(shè)備中故障信息間關(guān)系復(fù)雜多樣，在眾多信息中提取有效信息去除冗余信息，實(shí)現(xiàn)準(zhǔn)確、高效的故障診斷一直是學(xué)術(shù)界和工程界高度關(guān)注的問(wèn)題。本文結(jié)合國(guó)家自然科學(xué)基金項(xiàng)目，利用主元分析技術(shù)實(shí)現(xiàn)特征信息的有效提取，基于蟻群算法實(shí)現(xiàn)了設(shè)備故障快速高效的聚類診斷。主要工作如下： （1）歸納了旋轉(zhuǎn)機(jī)械的核心部件——轉(zhuǎn)子系統(tǒng)的典型故障機(jī)理，，論述了旋轉(zhuǎn)機(jī)械狀態(tài)信號(hào)的測(cè)量方法、故障特征提取方法及故障模式識(shí)別常用的幾種方法。 （2）論述了主元分析的基本原理，介紹了主元分析實(shí)現(xiàn)旋轉(zhuǎn)機(jī)械故障特征提取的相關(guān)理論，歸納了常用的主元選取方法。基于主元分析方法實(shí)現(xiàn)了旋轉(zhuǎn)機(jī)械故障特征的提取，分析了主元分析方法在旋轉(zhuǎn)機(jī)械故障特征提取中的信息遺漏的弊端，提出了一個(gè)自適應(yīng)主元選取的思路，定義了故障聚類正確率因子。 （3）介紹了蟻群算法尋優(yōu)的基本原理。通過(guò)蟻群算法在解決旅行商問(wèn)題時(shí)尋優(yōu)能力突出的特點(diǎn)對(duì)蟻群算法做改進(jìn)。利用蟻群算法較強(qiáng)的尋優(yōu)能力，以蟻群算法解決TSP問(wèn)題為原型，建立了基于蟻群算法的旋轉(zhuǎn)機(jī)械故障聚類診斷方法，設(shè)計(jì)了多因素多水平的正交實(shí)驗(yàn)對(duì)基于蟻群算法的旋轉(zhuǎn)機(jī)械故障聚類診斷方法中初始參數(shù)做了優(yōu)化。利用轉(zhuǎn)子故障試驗(yàn)臺(tái)測(cè)得的數(shù)據(jù)對(duì)基于蟻群算法的旋轉(zhuǎn)機(jī)械故障聚類診斷方法進(jìn)行了驗(yàn)證，證明能達(dá)到較好的聚類效果。 （4）提出了基于主元分析與蟻群算法的旋轉(zhuǎn)機(jī)械故障診斷方法模型。論述了基于核函數(shù)的主元分析旋轉(zhuǎn)機(jī)械特征提取方法的實(shí)現(xiàn)步驟，提出了自適應(yīng)主元選取方法的實(shí)現(xiàn)過(guò)程。建立了自適應(yīng)主元選取的基于主元分析與蟻群算法的旋轉(zhuǎn)機(jī)械故障聚類診斷方法，給出了該方法核心算法的偽代碼和程序流程圖。并結(jié)合轉(zhuǎn)子故障試驗(yàn)臺(tái)測(cè)試的數(shù)據(jù)對(duì)該方法的聚類診斷效果做了驗(yàn)證，證實(shí)了基于PCA與蟻群算法的旋轉(zhuǎn)機(jī)械故障診斷方法的有效性。
[Abstract]:Rotating machinery equipment is widely used in modern industry, once these equipment failure will bring huge economic losses. With the rapid development of computer technology, rotating machinery intelligent. The degree of integration is becoming higher and higher, the types and forms of fault appear more and more, and the amount of fault feature information should be considered more and more. Because of the complex relationship between fault information in large-scale complex equipment. Extracting effective information from a large number of information to remove redundant information to achieve accurate and efficient fault diagnosis has been a highly concerned problem in academic and engineering circles. This paper combined with the National Natural Science Foundation project. The effective extraction of feature information is realized by principal component analysis (PCA), and the fast and efficient clustering diagnosis of equipment fault is realized based on ant colony algorithm. The main work is as follows: 1) the typical fault mechanism of rotor system, which is the core component of rotating machinery, is summarized, and the measuring method of state signal of rotating machinery is discussed. Fault feature extraction method and several common methods of fault pattern recognition. 2) the basic principle of principal component analysis (PCA) is discussed, and the related theory of fault feature extraction for rotating machinery is introduced. Based on principal component analysis (PCA), the fault features of rotating machinery are extracted, and the disadvantages of PCA in fault feature extraction of rotating machinery are analyzed. An adaptive principal component selection method is proposed, and the fault clustering accuracy factor is defined. (3) the basic principle of ant colony algorithm is introduced. The ant colony algorithm is improved by its outstanding ability in solving traveling salesman problem. Based on ant colony algorithm (ACA) to solve the TSP problem, a rotating machinery fault cluster diagnosis method based on ant colony algorithm (ACA) is established. A multi-factor and multi-level orthogonal experiment was designed to optimize the initial parameters in the fault cluster diagnosis method of rotating machinery based on ant colony algorithm. The rotating machinery based on ant colony algorithm was optimized by using the data obtained from the rotor fault test rig. The method of fault clustering diagnosis is verified. It is proved that better clustering effect can be achieved. (4) the fault diagnosis method model of rotating machinery based on principal component analysis and ant colony algorithm is proposed, and the steps of feature extraction of rotating machinery based on kernel function are discussed. The realization process of adaptive principal component selection method is put forward, and a fault cluster diagnosis method for rotating machinery based on principal component analysis and ant colony algorithm is established. The pseudo code and program flow chart of the core algorithm of this method are given, and the clustering diagnosis effect of this method is verified with the test data of rotor fault test rig. The validity of the fault diagnosis method of rotating machinery based on PCA and ant colony algorithm is proved.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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