基于實(shí)時(shí)特征值的風(fēng)機(jī)振動(dòng)狀態(tài)監(jiān)測(cè)與數(shù)據(jù)挖掘的故障診斷研究
[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
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 徐曉峰;劉川槐;孟德彪;;600 MW機(jī)組非典型性一次風(fēng)機(jī)振動(dòng)故障診斷[J];安徽電力;2016年02期
2 楊秀文;;旋轉(zhuǎn)機(jī)械頻譜智能分析系統(tǒng)的研究與實(shí)現(xiàn)[J];山東工業(yè)技術(shù);2016年09期
3 陳釗;任瑞冬;符嬈;;一種基于時(shí)域?yàn)V波算法的振動(dòng)信號(hào)有效值計(jì)算方法研究[J];現(xiàn)代機(jī)械;2015年03期
4 王維友;楊璋;;振動(dòng)分析在風(fēng)機(jī)軸承故障診斷中的應(yīng)用[J];裝備維修技術(shù);2015年02期
5 李春芳;黃建民;;基于小波分析的轉(zhuǎn)子不平衡故障診斷與控制技術(shù)研究[J];宇航計(jì)測(cè)技術(shù);2015年02期
6 張文秀;武新芳;;風(fēng)電機(jī)組狀態(tài)監(jiān)測(cè)與故障診斷相關(guān)技術(shù)研究[J];電機(jī)與控制應(yīng)用;2014年02期
7 韓國良;;頻譜分析法對(duì)轉(zhuǎn)子不平衡故障的分析及診斷[J];化工管理;2014年03期
8 任學(xué)平;單立偉;;基于EMD-ICA和HMM的風(fēng)機(jī)故障分類方法[J];汽輪機(jī)技術(shù);2013年04期
9 馬中存;趙軍;張麗蓉;;旋轉(zhuǎn)機(jī)械動(dòng)靜碰摩故障的振動(dòng)監(jiān)測(cè)研究[J];機(jī)械工程師;2013年08期
10 吳興偉;;基于DDAGSVM的離心風(fēng)機(jī)振動(dòng)故障診斷[J];風(fēng)機(jī)技術(shù);2013年03期
相關(guān)博士學(xué)位論文 前3條
1 魯文波;基于聲場(chǎng)空間分布特征的機(jī)械故障診斷方法及其應(yīng)用研究[D];上海交通大學(xué);2012年
2 隋文濤;滾動(dòng)軸承表面損傷故障的特征提取與診斷方法研究[D];山東大學(xué);2011年
3 侯軍虎;基于多參數(shù)的風(fēng)機(jī)狀態(tài)監(jiān)測(cè)與故障診斷的研究[D];華北電力大學(xué)(河北);2004年
相關(guān)碩士學(xué)位論文 前10條
1 徐明林;基于小波降噪和經(jīng)驗(yàn)?zāi)B(tài)分解的滾動(dòng)軸承故障診斷[D];哈爾濱工業(yè)大學(xué);2013年
2 曹亭;火炮狀態(tài)診斷與應(yīng)急處理方法研究[D];南京理工大學(xué);2013年
3 何亮;基于EMD技術(shù)的滾動(dòng)軸承故障診斷研究[D];大連理工大學(xué);2012年
4 黃超勇;基于粒子群優(yōu)化支持向量機(jī)決策樹的齒輪箱故障診斷方法[D];太原理工大學(xué);2012年
5 周澤民;基于嵌入式系統(tǒng)的旋轉(zhuǎn)機(jī)械故障診斷儀的開發(fā)研究[D];南華大學(xué);2012年
6 楊玉婧;基于神經(jīng)網(wǎng)絡(luò)的旋轉(zhuǎn)機(jī)械振動(dòng)故障診斷的研究[D];華北電力大學(xué);2012年
7 許雪貴;基于WEB的機(jī)電設(shè)備遠(yuǎn)程監(jiān)測(cè)系統(tǒng)的應(yīng)用研究[D];電子科技大學(xué);2011年
8 王宏超;基于全矢譜的旋轉(zhuǎn)機(jī)械故障特征提取研究[D];鄭州大學(xué);2011年
9 徐華;基于LabVIEW和分形技術(shù)的狀態(tài)監(jiān)測(cè)與故障診斷系統(tǒng)的研究[D];武漢科技大學(xué);2008年
10 王立榮;設(shè)備振動(dòng)監(jiān)測(cè)分析診斷系統(tǒng)研究[D];華北電力大學(xué)(北京);2008年
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