發(fā)布時(shí)間：2018-10-15 09:45

【摘要】：風(fēng)機(jī)普遍應(yīng)用于石油、化工、電力、冶金等行業(yè),隨著設(shè)備長(zhǎng)時(shí)間的運(yùn)行,發(fā)生故障的概率大大上升,便可能使其停產(chǎn),會(huì)造成巨大的經(jīng)濟(jì)損失和安全隱患。因此,對(duì)風(fēng)機(jī)設(shè)備進(jìn)行狀態(tài)監(jiān)測(cè)與故障診斷具有極其重要的意義。本文詳細(xì)介紹了國內(nèi)外旋轉(zhuǎn)機(jī)械狀態(tài)監(jiān)測(cè)和故障診斷技術(shù)的發(fā)展現(xiàn)狀,發(fā)現(xiàn)了目前的研究現(xiàn)狀是:在振動(dòng)信號(hào)的監(jiān)測(cè)上國內(nèi)外都已有較成熟的方法和技術(shù),但是在故障診斷上常用的頻域方法由于計(jì)算量大,基本上仍然是離線式的。診斷結(jié)果的分析需要人工進(jìn)行,各種應(yīng)用方法的專業(yè)性強(qiáng),不易完成實(shí)時(shí)計(jì)算和在線分析判斷,缺少在線的、智能的方法和技術(shù)。隨著就地監(jiān)測(cè)設(shè)備成本的降低,對(duì)風(fēng)機(jī)設(shè)備安裝傳感器進(jìn)行運(yùn)行狀況監(jiān)測(cè)變成了可能。通過采集風(fēng)機(jī)設(shè)備全周期運(yùn)行的時(shí)域特征參數(shù),得到風(fēng)機(jī)在不同運(yùn)行狀態(tài)下的特征參數(shù)變化,形成風(fēng)機(jī)設(shè)備運(yùn)行特征參數(shù)數(shù)據(jù)庫,結(jié)合數(shù)據(jù)挖掘的方法,能夠從海量的數(shù)據(jù)中建立基于時(shí)域特征值的故障診斷模型。這樣做的優(yōu)點(diǎn)是由于時(shí)域特征值是實(shí)時(shí)的、在線的,故障診斷模型也將是實(shí)時(shí)的、在線的,同時(shí)故障診斷的準(zhǔn)確率也將隨著數(shù)據(jù)庫的擴(kuò)充以及數(shù)據(jù)挖掘方法的改進(jìn)而不斷提高。本文為解決CAP1400安全殼再循環(huán)冷卻風(fēng)機(jī)無狀態(tài)監(jiān)測(cè)的問題,主要進(jìn)行了以下工作:圍繞振動(dòng)的基本概念、旋轉(zhuǎn)機(jī)械典型振動(dòng)故障研究成果以及振動(dòng)信號(hào)的特征提取和分析方法作了介紹,并且根據(jù)國際國內(nèi)以及行業(yè)標(biāo)準(zhǔn)選取了部分時(shí)域的典型特征值作了具體的分析,為下一步的故障診斷方法建立了基礎(chǔ);其次,設(shè)計(jì)開發(fā)了風(fēng)機(jī)振動(dòng)狀態(tài)監(jiān)測(cè)平臺(tái)軟件部分,并且進(jìn)行了風(fēng)機(jī)運(yùn)行狀態(tài)監(jiān)測(cè)試驗(yàn),驗(yàn)證和完善了振動(dòng)狀態(tài)監(jiān)測(cè)平臺(tái)的功能,完成了風(fēng)機(jī)特征參數(shù)的計(jì)算和數(shù)據(jù)存儲(chǔ),建立了風(fēng)機(jī)振動(dòng)特征值數(shù)據(jù)庫;最后,依托于振動(dòng)特征值數(shù)據(jù)庫,對(duì)特征值參數(shù)進(jìn)行了數(shù)據(jù)挖掘,建立了不同振動(dòng)故障的特征值敏感等級(jí)分布表,確定了不同特征參數(shù)之間的潛在關(guān)系,評(píng)估預(yù)測(cè)了風(fēng)機(jī)運(yùn)行狀態(tài)。在此基礎(chǔ)上,開發(fā)了基于實(shí)時(shí)特征值數(shù)據(jù)挖掘的風(fēng)機(jī)振動(dòng)故障診斷模型;應(yīng)用該振動(dòng)故障診斷模型對(duì)實(shí)際故障進(jìn)行在線分析和診斷。本論文的診斷方法,在試驗(yàn)臺(tái)架上獲得了有效的應(yīng)用,表明了基于數(shù)據(jù)挖掘的診斷方法能夠?qū)崟r(shí)有效的完成振動(dòng)故障的在線分析和不同故障原因的診斷,對(duì)實(shí)現(xiàn)機(jī)械行業(yè)廣泛應(yīng)用的風(fēng)機(jī)、水泵等旋轉(zhuǎn)機(jī)械的現(xiàn)場(chǎng)實(shí)時(shí)監(jiān)測(cè)和振動(dòng)智能遠(yuǎn)程診斷,具有工程應(yīng)用價(jià)值和推廣價(jià)值。
[Abstract]:Fan is widely used in petroleum, chemical, electric power, metallurgical and other industries. With the equipment running for a long time, the probability of failure is greatly increased, which may make it stop production, which will cause huge economic losses and safety risks. Therefore, the condition monitoring and fault diagnosis of fan equipment is of great significance. In this paper, the status quo of status monitoring and fault diagnosis of rotating machinery at home and abroad is introduced in detail. It is found that there are mature methods and techniques for vibration signal monitoring both at home and abroad. However, the frequency-domain method commonly used in fault diagnosis is still offline because of the large amount of calculation. The analysis of diagnosis results needs to be carried out manually, and all kinds of application methods are highly professional. It is difficult to complete real-time calculation and on-line analysis and judgment, and lacks on-line and intelligent methods and techniques. With the reduction of the cost of local monitoring equipment, it is possible to monitor the operating condition of fan equipment installation sensors. By collecting the time domain characteristic parameters of the fan equipment running in the whole period, the change of the fan characteristic parameters under different running conditions is obtained, and the database of the fan equipment running characteristic parameters is formed, and the method of data mining is combined. A fault diagnosis model based on time domain eigenvalue can be established from massive data. The advantage of this method is that the time domain eigenvalue is real-time and on-line, and the fault diagnosis model will be real-time and on-line. At the same time, the accuracy of fault diagnosis will be improved with the expansion of database and the improvement of data mining method. In order to solve the problem of stateless monitoring of CAP1400 containment recirculation cooling fan, the main work of this paper is as follows: around the basic concept of vibration, The research results of typical vibration faults of rotating machinery and the methods of feature extraction and analysis of vibration signals are introduced, and some typical characteristic values in time domain are selected according to international and domestic standards and industry standards for specific analysis. The foundation of the next fault diagnosis method is established. Secondly, the software of fan vibration condition monitoring platform is designed and developed, and the fan running condition monitoring test is carried out to verify and perfect the function of the vibration condition monitoring platform. The calculation and data storage of fan characteristic parameters are completed, and the database of fan vibration eigenvalue is established. Finally, based on the vibration characteristic value database, the data mining of the characteristic value parameter is carried out. The distribution table of eigenvalue sensitivity level of different vibration faults is established, the potential relationship between different characteristic parameters is determined, and the operating state of fan is evaluated and predicted. On this basis, a fault diagnosis model of fan vibration based on real-time eigenvalue data mining is developed, and the fault diagnosis model is used to analyze and diagnose the actual faults online. The method of diagnosis in this paper has been effectively applied on the test-bed, which shows that the diagnosis method based on data mining can effectively complete the on-line analysis of vibration faults and the diagnosis of different fault causes in real time. It has engineering application value and popularizing value to realize field real time monitoring and intelligent remote diagnosis of vibration of rotating machinery such as fan pump and so on which are widely used in mechanical industry.
【學(xué)位授予單位】：上海發(fā)電設(shè)備成套設(shè)計(jì)研究院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH43;TP311.13

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本文編號(hào)：2272146