本文關(guān)鍵詞：潛油螺桿泵工作特性分析及在線故障診斷研究　出處：《西安石油大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 潛油螺桿泵 工作特性 故障 診斷

【摘要】：潛油螺桿泵具有能耗低、效率高、占地面積少并且從根本上解決了地面驅(qū)動螺桿泵桿斷、桿管偏磨的問題,在油田上數(shù)量逐年增加,然而,由于缺乏在線實(shí)時(shí)監(jiān)控潛油螺桿泵井下機(jī)組工況的方法,因而難以及時(shí)準(zhǔn)確地判斷出井下機(jī)組的工況,這嚴(yán)重影響了機(jī)組的正常生產(chǎn)和使用壽命,大大限制了其在油田上的進(jìn)一步推廣,為了保障潛油螺桿泵安全高效生產(chǎn)、加大推廣力度以及適應(yīng)數(shù)字化油田建設(shè)的需要,本文開展了潛油螺桿泵工作特性分析及在線故障診斷研究。本文首先分析了潛油螺桿泵工作特性,并在此基礎(chǔ)上,運(yùn)用小波包和RBF神經(jīng)網(wǎng)絡(luò)進(jìn)行了故障診斷,主要內(nèi)容如下:(1)以潛油螺桿泵系統(tǒng)為研究對象,分析了泵的運(yùn)動特性、力學(xué)特性、排量特性等工作特性,并運(yùn)用節(jié)點(diǎn)分析法建立了潛油電機(jī)有功功率和泵功耗之間的數(shù)學(xué)模型。(2)應(yīng)用ANSYS軟件對潛油螺桿泵進(jìn)行了有限元分析,得到了不同工況對泵工作特性的影響規(guī)律。(3)基于GPRS遠(yuǎn)程在線傳輸網(wǎng)絡(luò),依據(jù)地面驅(qū)動螺桿泵采油系統(tǒng)常見故障類型和診斷方法,結(jié)合潛油螺桿泵采油系統(tǒng)自身結(jié)構(gòu)特點(diǎn)和工作特性,提出了以潛油電機(jī)有功功率為研究對象、小波包和RBF神經(jīng)網(wǎng)絡(luò)相結(jié)合的潛油螺桿泵在線故障診斷方案。(4)運(yùn)用小波包提取了潛油電機(jī)有功功率信號,搭建了潛油螺桿泵故障樣本庫,利用Matlab軟件建立了RBF神經(jīng)網(wǎng)絡(luò),并確定了網(wǎng)絡(luò)的訓(xùn)練參數(shù),最終完成了網(wǎng)絡(luò)的訓(xùn)練和測試。(5)以Visual Basic 6.0為平臺,開發(fā)了潛油螺桿泵在線故障診斷系統(tǒng)軟件,實(shí)現(xiàn)了人機(jī)交互、在線監(jiān)測和故障診斷的功能,為潛油螺桿泵安全、高效生產(chǎn)提供了可靠保證。
[Abstract]:Submersible screw pump has low energy consumption, high efficiency, less area, and fundamentally solved the ground driven screw pump rod broken, rod pipe wear problem, the number of oil field increased year by year, however. Due to the lack of on-line real-time monitoring of submersible screw pump downhole operating conditions, it is difficult to determine the working conditions of downhole units in time and accurately, which seriously affects the normal production and service life of the units. In order to ensure the safe and efficient production of the submersible screw pump, to increase the promotion and adapt to the needs of the construction of digital oil field. In this paper, the working characteristics of submersible screw pump and on-line fault diagnosis are studied. Firstly, the working characteristics of submersible screw pump are analyzed, and on the basis of this, the working characteristics of submersible screw pump are analyzed. Wavelet packet and RBF neural network are used for fault diagnosis. The main contents are as follows: 1) taking the submersible screw pump system as the research object, the working characteristics of the pump such as motion, mechanical characteristics, displacement characteristics and so on are analyzed. The mathematical model between active power and pump power consumption of submersible motor is established by using node analysis method. The finite element analysis of submersible screw pump is carried out by using ANSYS software. The influence of different working conditions on pump performance is obtained. Based on GPRS remote online transmission network, common fault types and diagnosis methods of surface driven screw pump oil recovery system are obtained. Combined with the structural characteristics and working characteristics of the submersible screw pump oil recovery system, the active power of the submersible motor is proposed as the research object. Wavelet packet and RBF neural network combined submersible screw pump on-line fault diagnosis scheme. 4) using wavelet packet to extract submersible motor active power signal, build submersible screw pump fault sample database. The RBF neural network is established by using Matlab software, and the training parameters of the network are determined. Finally, the network training and testing. 5) based on Visual Basic 6.0, the online fault diagnosis software of submersible screw pump is developed, and the man-machine interaction is realized. The functions of on-line monitoring and fault diagnosis provide a reliable guarantee for the safety and efficient production of submersible screw pump.
【學(xué)位授予單位】：西安石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TE933.3

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