本文選題：復(fù)雜網(wǎng)絡(luò) + 社團(tuán)聚類(lèi)��； 參考：《湖南科技大學(xué)》2014年碩士論文

【摘要】：隨著工業(yè)技術(shù)的進(jìn)步，大型復(fù)雜機(jī)械正朝著大型化、復(fù)雜化、集成化發(fā)展，設(shè)備一旦發(fā)生重大故障將嚴(yán)重影響工業(yè)生產(chǎn)，造成重大經(jīng)濟(jì)損失。因此，對(duì)大型復(fù)雜機(jī)械進(jìn)行準(zhǔn)確故障診斷，確保機(jī)械設(shè)備安全準(zhǔn)確運(yùn)行是當(dāng)前機(jī)械故障診斷領(lǐng)域的研究熱點(diǎn)之一。復(fù)雜網(wǎng)絡(luò)是近年興起的新的研究方法，是一種用來(lái)描述復(fù)雜系統(tǒng)的重要模型和工具。它將系統(tǒng)中的元素視為網(wǎng)絡(luò)節(jié)點(diǎn)，節(jié)點(diǎn)之間的連接邊表示元素之間的關(guān)系，通過(guò)對(duì)節(jié)點(diǎn)與邊的分析挖掘網(wǎng)絡(luò)中的自組織、自相似、小世界等特性，其中小世界性表現(xiàn)為節(jié)點(diǎn)相互連接組成的小集合，這些集合內(nèi)部連接緊密，與集合外部連接較少，，這種特性又稱(chēng)為社團(tuán)特性，這正好與故障診斷領(lǐng)域同類(lèi)故障樣本之間聯(lián)系緊密、不同故障樣本之間聯(lián)系稀疏的特性相對(duì)應(yīng)。論文將故障樣本視為復(fù)雜網(wǎng)絡(luò)中的節(jié)點(diǎn)，建立故障樣本復(fù)雜網(wǎng)絡(luò)模型，并開(kāi)展社團(tuán)聚類(lèi)診斷分析。主要工作如下： （1）開(kāi)展了故障樣本復(fù)雜網(wǎng)絡(luò)社團(tuán)特性分析，確定相似度函數(shù)、網(wǎng)絡(luò)邊權(quán)、邊閾值等因數(shù)，建立故障樣本網(wǎng)絡(luò)模型；研究了基于互信息評(píng)價(jià)網(wǎng)絡(luò)模型中重要節(jié)點(diǎn)的計(jì)算方法，并驗(yàn)證了該方法較其他方法的優(yōu)越性。 （2）研究了基于復(fù)雜網(wǎng)絡(luò)社團(tuán)聚類(lèi)的故障診斷方法。應(yīng)用復(fù)雜網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)特性，將網(wǎng)絡(luò)劃分為若干個(gè)社團(tuán)，利用社團(tuán)模塊性合并指標(biāo)變化開(kāi)展社團(tuán)聚類(lèi)，最后合并的社團(tuán)對(duì)應(yīng)不同故障類(lèi)型，實(shí)現(xiàn)診斷；以滾動(dòng)軸承故障診斷實(shí)例驗(yàn)證了方法的有效性。 （3）研究了基于復(fù)雜網(wǎng)絡(luò)社團(tuán)聚類(lèi)的改進(jìn)K-means聚類(lèi)診斷方法。針對(duì)K-means聚類(lèi)算法依賴(lài)于初始聚類(lèi)數(shù)K值和初始聚類(lèi)中心的不足，利用復(fù)雜網(wǎng)絡(luò)社團(tuán)聚類(lèi)為K-means聚類(lèi)算法確定K值，通過(guò)計(jì)算網(wǎng)絡(luò)節(jié)點(diǎn)關(guān)聯(lián)度選取重要節(jié)點(diǎn)作為初始聚類(lèi)中心，開(kāi)展聚類(lèi)診斷；有效克服了K-means聚類(lèi)算法中K值和初始聚類(lèi)中心選取困難的問(wèn)題；并以滾動(dòng)軸承故障診斷實(shí)例，驗(yàn)證了該方法的有效性。 （4）研究了基于復(fù)雜網(wǎng)絡(luò)社團(tuán)聚類(lèi)的復(fù)合故障特征分離方法。應(yīng)用經(jīng)驗(yàn)?zāi)B(tài)分解將復(fù)合故障信號(hào)分解為若干個(gè)不同頻段的IMF分量，將每個(gè)IMF分量視為網(wǎng)絡(luò)中社團(tuán)，進(jìn)行同類(lèi)社團(tuán)合并，最后得到對(duì)應(yīng)不同單一故障的各個(gè)社團(tuán)，實(shí)現(xiàn)復(fù)合故障的有效分離。以轉(zhuǎn)子不平衡和軸承內(nèi)圈復(fù)合故障分離實(shí)例、軸承內(nèi)圈和滾珠復(fù)合故障分離實(shí)例，驗(yàn)證了方法的有效性。
[Abstract]:With the progress of industrial technology, large and complex machinery is facing large, complicated and integrated development. Once the equipment has serious failure, it will seriously affect industrial production and cause significant economic loss. Therefore, accurate fault diagnosis of large and complex machinery and ensuring the safe and accurate operation of mechanical equipment are the current field of mechanical fault diagnosis. Complex network is a new research method in recent years. It is an important model and tool used to describe complex systems. It treats the elements in the system as network nodes, the connections between nodes represent the relationship between elements and the self organization, self similarity and small world in the network through the analysis of the nodes and edges. The small world is characterized by small world representation of small sets of nodes connected to each other. These sets are closely connected inside and are less connected to the set. This feature is also called community characteristics, which is closely related to the similar fault samples in the fault diagnosis field. Fault samples are considered as nodes in complex networks. A complex network model of fault samples is established, and community clustering diagnosis analysis is carried out.
(1) carry out the analysis of the community characteristics of the complex network of fault samples, determine the similarity function, the network edge weight, edge threshold and other factors, establish the fault sample network model, study the calculation method of the important nodes in the mutual information evaluation network model, and verify the superiority of the method compared with the other methods.
(2) the fault diagnosis method based on complex network community clustering is studied. Using the complex network community structure characteristics, the network is divided into several societies, and the community clustering is carried out by the changes of the association modular merging index. The final merged community corresponds to the different fault types, and the diagnosis is verified by the rolling bearing fault diagnosis example. The validity of the law.
(3) the improved K-means clustering diagnosis method based on complex network community clustering is studied. The K-means clustering algorithm relies on the initial clustering number K value and the shortage of the initial cluster center. The complex network community clustering is used to determine the K value by the K-means clustering algorithm, and the important node is selected as the initial cluster center through the calculation of the node association degree of the network. The clustering diagnosis is carried out, and the problem of selecting the K value and the initial cluster center in the K-means clustering algorithm is effectively overcome, and the validity of the method is verified by a fault diagnosis example of rolling bearing.
(4) the complex fault feature separation method based on complex network community clustering is studied. The complex fault signal is decomposed into IMF components of several different frequency bands by using empirical mode decomposition. Each IMF component is regarded as a community in the network, and the similar associations are merged. Finally, a complex fault is obtained to achieve a complex fault. The effective separation of the rotor imbalance and bearing inner ring compound fault is illustrated. An example of bearing inner ring and ball compound fault separation is used to verify the effectiveness of the method.
【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】：2014
【分類(lèi)號(hào)】：TH165.3

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