【摘要】：往復(fù)泵作為工業(yè)領(lǐng)域重要的往復(fù)設(shè)備,廣泛應(yīng)用于石油鉆井、油氣開采等生產(chǎn)領(lǐng)域。液力端作為往復(fù)泵的薄弱環(huán)節(jié)在使用當中經(jīng)常發(fā)生故障,由于液力端的結(jié)構(gòu)復(fù)雜、激勵源多,導(dǎo)致液力端的故障診斷比較困難。開展對往復(fù)泵液力端的故障診斷研究工作對于提高油田往復(fù)泵的使用效率,保證安全生產(chǎn)具有十分重要的意義。柱塞磨損故障和泵閥組件故障是本文診斷研究的重點,本文利用時域統(tǒng)計指標診斷方法對柱塞磨損故障進行了診斷；同時,利用小波包分析方法提取了泵閥組件的故障特征,在MATLAB中創(chuàng)建了BP神經(jīng)網(wǎng)絡(luò),并利用創(chuàng)建的人工神經(jīng)網(wǎng)絡(luò)實現(xiàn)了液力端故障的智能識別。這些診斷方法可較好地用于往復(fù)泵液力端的故障診斷當中。 本文的研究工作主要體現(xiàn)在以下幾個方面： 首先,在廣泛調(diào)研國內(nèi)外文獻以及現(xiàn)場資料的基礎(chǔ)上,分析了往復(fù)泵液力端的工作原理、常見故障及故障原因。建立了簡化后閥蓋振動的力學(xué)模型,論述了利用振動信號進行液力端故障診斷的可行性。指出了液力端振動信號的非平穩(wěn)性和故障是促使振動信號發(fā)生變化的主要原因。設(shè)計了液力端的故障實驗方案,在3DS-1/12.5型往復(fù)泵上進行了故障實驗； 其次,針對液力端振動信號的非平穩(wěn)性特點,對柱塞磨損故障采用了時域統(tǒng)計指標診斷方法,計算出了各指標的重要度系數(shù),建立了柱塞磨損程度的診斷模型,并通過故障程度值較為直觀地反映了柱塞的磨損程度；針對泵閥振動信號具有非平穩(wěn)特性、故障特征提取難度大的特點,在對幾種時頻分析方法進行對比分析的基礎(chǔ)上,本文引入了小波和小波包變換方法,對振動信號進行小波包分析；提出了能量最大差別法來確定小波包分解方案,通過建立“頻率—能量—故障”的診斷模型,有效提取出了泵閥組件發(fā)生不同類型、不同程度時的故障特征。 然后,詳細介紹了BP神經(jīng)網(wǎng)絡(luò)的算法原理,在MATLAB中利用BP網(wǎng)絡(luò)的改進措施實現(xiàn)了BP網(wǎng)絡(luò)的創(chuàng)建與訓(xùn)練,通過所創(chuàng)建的神經(jīng)網(wǎng)絡(luò)實現(xiàn)了液力端故障的智能識別,并且診斷準確率均達到了90%以上。 最后,針對實驗中存在的問題和不足,設(shè)計了往復(fù)泵液力端故障的振動診斷方案與整體性能診斷方案,并對往復(fù)泵液力端測試硬件系統(tǒng)的方案進行了設(shè)計,提高了診斷測試方法在油田現(xiàn)場的可實施性。
[Abstract]:As an important reciprocating equipment in industry, reciprocating pump is widely used in oil drilling, oil and gas production. As the weak link of reciprocating pump, the hydraulic end often breaks down in use. Because of the complex structure of the hydraulic end and many exciting sources, it is difficult to diagnose the fault of the hydraulic end. It is very important to develop the fault diagnosis of reciprocating pump hydraulic end for improving the efficiency of oil field reciprocating pump and ensuring safe production. The fault of plunger wear and the fault of pump valve assembly are the focal points of this paper. The fault diagnosis of plunger wear is carried out by using time domain statistical index diagnosis method, and the fault characteristics of pump valve assembly are extracted by wavelet packet analysis method. The BP neural network is established in MATLAB, and the intelligent identification of hydraulic end faults is realized by using the created artificial neural network. These methods can be used in fault diagnosis of hydraulic end of reciprocating pump. The research work of this paper is mainly reflected in the following aspects: firstly, on the basis of extensive investigation of domestic and foreign literature and field data, the working principle, common faults and fault causes of hydraulic end of reciprocating pump are analyzed. A simplified mechanical model of valve cover vibration is established, and the feasibility of hydraulic end fault diagnosis using vibration signal is discussed. It is pointed out that the non-stationarity and fault of the vibration signal at the hydraulic end are the main reasons for the change of the vibration signal. The fault test scheme of hydraulic end is designed, and the fault experiment is carried out on 3DS-1/12.5 reciprocating pump. Secondly, according to the non-stationary characteristic of vibration signal at hydraulic end, the time-domain statistical index diagnosis method is used to diagnose the plunger wear fault. The importance coefficient of each index is calculated, the diagnostic model of piston wear degree is established, and the wear degree of plunger is intuitively reflected by the value of fault degree, and the vibration signal of pump valve has non-stationary characteristic. On the basis of comparing and analyzing several time-frequency analysis methods, wavelet transform and wavelet packet transform are introduced to analyze vibration signal. In this paper, the maximum difference method of energy is proposed to determine the wavelet packet decomposition scheme. By establishing a diagnosis model of "frequency-energy-fault", the fault characteristics of different types and different degrees of pump valve components are extracted effectively. Then, the algorithm principle of BP neural network is introduced in detail, and the establishment and training of BP network are realized by using the improvement measure of BP network in MATLAB, and the intelligent identification of hydraulic end fault is realized by the created neural network. And the diagnostic accuracy is over 90%. Finally, aiming at the problems and shortcomings in the experiment, the vibration diagnosis scheme and the overall performance diagnosis scheme of hydraulic end fault of reciprocating pump are designed, and the scheme of testing hardware system of hydraulic end of reciprocating pump is designed. The practicability of diagnosis and test method in oil field is improved.
【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3

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