本文選題：旋轉(zhuǎn)機(jī)械 + 故障診斷　； 參考：《北京化工大學(xué)》2011年碩士論文

【摘要】：自工業(yè)革命以來(lái),社會(huì)生產(chǎn)水平得到飛速的發(fā)展,為了滿足人們對(duì)產(chǎn)品日益增長(zhǎng)的需求,工業(yè)自動(dòng)化生產(chǎn)水平不斷提高。在這種背景下,生產(chǎn)設(shè)備日趨向大型化、復(fù)雜化、自動(dòng)化、連續(xù)性生產(chǎn)方向發(fā)展。一旦工廠關(guān)鍵機(jī)組出現(xiàn)故障停機(jī),往往造成重大安全事故和嚴(yán)重經(jīng)濟(jì)損失,特別是在石化企業(yè),造成的損失往往數(shù)以百萬(wàn)計(jì)。因此,如何保證生產(chǎn)線能夠安全穩(wěn)定的運(yùn)行就成了各大企業(yè)一個(gè)非常重要的課題,設(shè)備故障診斷技術(shù)也是在這一需求背景下發(fā)展而來(lái)的。在工業(yè)生產(chǎn)中,應(yīng)用先進(jìn)的設(shè)備故障診斷技術(shù)可以有效準(zhǔn)確的對(duì)設(shè)備進(jìn)行故障診斷,分析其故障機(jī)理與故障原因,從而有效改善設(shè)備維修效率,提高企業(yè)經(jīng)濟(jì)效益,降低設(shè)備維護(hù)成本并減少企業(yè)發(fā)生重大安全事故幾率,因此具有重要的經(jīng)濟(jì)意義和應(yīng)用價(jià)值。 本論文以旋轉(zhuǎn)機(jī)械智能故障診斷技術(shù)為研究對(duì)象,結(jié)合專家系統(tǒng)技術(shù),對(duì)旋轉(zhuǎn)機(jī)械智能故障診斷技術(shù)進(jìn)行了研究,全文主要內(nèi)容主要分為如下幾部分。 第一章緒論部分主要介紹本課題來(lái)源和研究意義,并簡(jiǎn)單概述了故障診斷技術(shù)的發(fā)展歷史和專家系統(tǒng)在故障診斷領(lǐng)域的應(yīng)用現(xiàn)狀,以及當(dāng)前智能故障診斷技術(shù)的發(fā)展方向和前景,并在最后提出本課題的研究?jī)?nèi)容和研究目的。 第二章主要針對(duì)本課題的研究對(duì)象,系統(tǒng)闡述了專家系統(tǒng)領(lǐng)域的經(jīng)典前向匹配算法Rete算法的發(fā)展歷史以及算法數(shù)據(jù)流網(wǎng)絡(luò)實(shí)現(xiàn)原理,并根據(jù)本課題需求用偽代碼的形式描述了該算法的具體實(shí)現(xiàn)。 第三章主要針對(duì)旋轉(zhuǎn)機(jī)械常見(jiàn)故障包括轉(zhuǎn)子動(dòng)平衡、轉(zhuǎn)子不對(duì)中、轉(zhuǎn)子彎曲等常見(jiàn)故障的特征和機(jī)理進(jìn)行深入分析,并在最后將旋轉(zhuǎn)機(jī)械常見(jiàn)故障以表格的形式進(jìn)行了整理,這也是本課題專家系統(tǒng)知識(shí)庫(kù)的建立基礎(chǔ)。旋轉(zhuǎn)機(jī)械本身結(jié)構(gòu)復(fù)雜,且在現(xiàn)場(chǎng)經(jīng)常處理連續(xù)工作狀態(tài),因此其故障具有多樣性和復(fù)雜性的特點(diǎn),同一種故障常常會(huì)表現(xiàn)出多種征兆,一種征兆也往往是多種故障的疊加的結(jié)果。因此對(duì)旋轉(zhuǎn)機(jī)械常見(jiàn)故障進(jìn)行分析整理具有重要的實(shí)際意義, 第四章在前文內(nèi)容基礎(chǔ)上,利用前向快速匹配算法Rete算法作為專家系統(tǒng)規(guī)則推理算法,以第三章所總結(jié)的故障征兆表作為基礎(chǔ)整理的知識(shí)規(guī)則庫(kù),利用Java語(yǔ)言和Eclipse開(kāi)發(fā)平臺(tái)設(shè)計(jì)并完成旋轉(zhuǎn)機(jī)械故障診斷專家系統(tǒng)。 第五章對(duì)本課題的完成情況進(jìn)行了回顧,并指出了本課題研究過(guò)程中的缺陷以及對(duì)下一步研究工作的展望與建議。
[Abstract]:Since the Industrial Revolution, the level of social production has developed rapidly. In order to meet the increasing demand for products, the level of industrial automation production has been improved. In this context, production equipment tends to be large, complex, automatic, continuous production direction. Once the critical units of the plant are shut down, they often cause serious safety accidents and serious economic losses, especially in petrochemical enterprises, which often result in millions of losses. Therefore, how to ensure the safe and stable operation of the production line has become a very important issue for the large enterprises, and the equipment fault diagnosis technology has been developed under the background of this demand. In industrial production, the application of advanced equipment fault diagnosis technology can effectively and accurately diagnose the equipment, analyze its fault mechanism and cause, thus effectively improve the equipment maintenance efficiency and increase the economic benefit of the enterprise. It has important economic significance and application value to reduce the cost of equipment maintenance and reduce the probability of major safety accidents in enterprises. This paper takes the intelligent fault diagnosis technology of rotating machinery as the research object and combines the expert system technology to study the intelligent fault diagnosis technology of rotating machinery. The main content of this paper is divided into the following parts. The first chapter introduces the origin and research significance of this topic, and briefly summarizes the history of fault diagnosis technology and the application of expert system in fault diagnosis field. And the development direction and prospect of intelligent fault diagnosis technology at present, and finally put forward the research content and research purpose of this subject. In the second chapter, aiming at the research object of this subject, the development history of the classical forward matching algorithm Rete algorithm and the realization principle of the algorithm data stream network in the field of expert system are systematically expounded. The implementation of the algorithm is described in the form of pseudo code according to the requirements of this paper. The third chapter mainly analyzes the characteristics and mechanism of the common faults of rotating machinery, such as rotor dynamic balance, rotor misalignment, rotor bending and so on. At last, the common faults of rotating machinery are sorted out in the form of table. This is also the foundation of the expert system knowledge base. The rotating machinery itself is complex in structure and often deals with the continuous working state in the field, so its faults have the characteristics of diversity and complexity, the same kind of faults often show many kinds of symptoms. A symptom is also often the result of a combination of failures. Therefore, it is of great practical significance to analyze and sort out the common faults of rotating machinery. In chapter 4, the forward fast matching algorithm, Rete algorithm, is used as the expert system rule reasoning algorithm based on the previous contents. Based on the fault symptom table summarized in Chapter 3, a fault diagnosis expert system for rotating machinery is designed and completed by using Java language and Eclipse development platform. The fifth chapter reviews the completion of the project and points out the defects in the research process and the prospects and suggestions for the next research work.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3;TP182

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