【摘要】：在石油化工領(lǐng)域中,往復(fù)壓縮機(jī)主要負(fù)責(zé)油氣煉化和天然氣、乙烯等不穩(wěn)定氣體的運(yùn)輸工作,其零部件易發(fā)生腐蝕破壞。除此之外,往復(fù)壓縮機(jī)的構(gòu)造復(fù)雜,并經(jīng)常處于連續(xù)工作狀態(tài),因此許多易損零部件易發(fā)生疲勞破壞,壽命很短,導(dǎo)致故障發(fā)生頻率高,故障類(lèi)型復(fù)雜多樣。為降低事故發(fā)生率,確保工作人員人身安全,降低設(shè)備的購(gòu)買(mǎi)、維修成本,往復(fù)壓縮機(jī)故障檢測(cè)與診斷技術(shù)成為了人們研究的熱點(diǎn)之一。針對(duì)往復(fù)壓縮機(jī)振動(dòng)加速度信號(hào)的非線(xiàn)性、非平穩(wěn)等特性,將時(shí)間信息引入多重分形理論,提出了一種基于時(shí)變奇異譜的往復(fù)壓縮機(jī)故障特征提取方法,用以描述振動(dòng)信號(hào)的整體和細(xì)節(jié)信息,并利用支持向量機(jī)(SVM)進(jìn)行模式識(shí)別和分類(lèi),結(jié)果表明該方法能夠更加詳細(xì)精準(zhǔn)地表達(dá)故障信息,有利于提高故障診斷的精確度。首先,對(duì)往復(fù)壓縮機(jī)故障診斷技術(shù)進(jìn)行概括、研究和對(duì)比,提出本文故障特征提取方法的研究思路;對(duì)智能模式識(shí)別方法研究現(xiàn)狀進(jìn)行概括、研究和對(duì)比,為驗(yàn)證特征提取方法的效果提供了方法和依據(jù)。然后,對(duì)往復(fù)壓縮機(jī)的基本結(jié)構(gòu)、主要零部件、工作原理和工作循環(huán)過(guò)程進(jìn)行分析,總結(jié)故障形式,并研究其故障機(jī)理,并根據(jù)工作過(guò)程中關(guān)鍵零部件的受力狀態(tài)建立力學(xué)模型;利用相空間重構(gòu)理論,計(jì)算出能夠定量表征混沌性的關(guān)聯(lián)維數(shù),來(lái)說(shuō)明其振動(dòng)信號(hào)具有混沌性特征。接著,介紹分形及多重分形理論和算法,闡述多重分形譜中重要譜參數(shù)在往復(fù)壓縮機(jī)故障信號(hào)分析中代表含義;將時(shí)間信息引入多重分形理論,建立時(shí)變奇異譜理論模型,并提出根據(jù)往復(fù)壓縮機(jī)的工作過(guò)程來(lái)實(shí)現(xiàn)時(shí)變奇異譜的計(jì)算方法;對(duì)支持向量機(jī)分類(lèi)理論進(jìn)行研究,根據(jù)所提取故障特征向量的特點(diǎn),選擇“一對(duì)多”分類(lèi)法為基礎(chǔ)建立SVM模型,并對(duì)其中參數(shù)進(jìn)行優(yōu)選和設(shè)置。最后,將基于時(shí)變奇異譜的故障特征提取方法應(yīng)用于D122往復(fù)壓縮機(jī)的故障診斷中。設(shè)定往復(fù)壓縮機(jī)的診斷流程,包括振動(dòng)加速度信號(hào)采集,小波分解結(jié)合LMD分解的信號(hào)降噪處理,時(shí)變奇異譜提取故障特征向量和利用SVM分類(lèi)器進(jìn)行故障類(lèi)型分類(lèi)。分類(lèi)結(jié)果表明,氣閥故障分類(lèi)準(zhǔn)確率達(dá)到100%,軸承故障分類(lèi)準(zhǔn)確率達(dá)到93%,驗(yàn)證了基于時(shí)變奇異譜方法提取故障特征的有效性,說(shuō)明采用該方法提取的特征向量能夠準(zhǔn)確區(qū)分往復(fù)壓縮機(jī)的主要故障類(lèi)型。
[Abstract]:In the field of petrochemical industry, reciprocating compressors are mainly responsible for oil and gas refining and transportation of unstable gases such as natural gas and ethylene. In addition, the reciprocating compressor is complex in structure and often in continuous working state. Therefore, many vulnerable parts are prone to fatigue damage, life is very short, resulting in high frequency of fault occurrence and complex fault types. In order to reduce the incidence of accidents, ensure the personal safety of staff, reduce the purchase of equipment, maintenance costs, reciprocating compressor fault detection and diagnosis technology has become one of the hot spots. In view of the nonlinear and non-stationary characteristics of vibration acceleration signal of reciprocating compressor, a method of fault feature extraction based on time-varying singular spectrum is proposed by introducing time information into multifractal theory. It is used to describe the whole and detailed information of vibration signal, and the support vector machine (SVM) is used for pattern recognition and classification. The results show that the proposed method can express the fault information in more detail and accurately, which is helpful to improve the accuracy of fault diagnosis. First of all, the fault diagnosis technology of reciprocating compressor is summarized, studied and compared, and the research train of thought of fault feature extraction method in this paper is put forward, and the research status of intelligent pattern recognition method is summarized, studied and compared. It provides the method and basis for verifying the effect of feature extraction method. Then, the basic structure, main parts, working principle and working cycle process of reciprocating compressor are analyzed, the fault forms are summarized, and the fault mechanism is studied. The mechanical model is established according to the stress state of the key parts in the working process, and the correlation dimension which can represent chaos quantitatively is calculated by using the theory of phase space reconstruction, which shows that the vibration signal is chaotic. Then, the fractal and multifractal theory and algorithm are introduced, the meaning of important spectral parameters in multifractal spectrum is expounded in the fault signal analysis of reciprocating compressor, the time information is introduced into multifractal theory, and the time-varying singular spectrum theory model is established. According to the working process of reciprocating compressor, the calculation method of time-varying singular spectrum is put forward, and the classification theory of support vector machine is studied. The SVM model is built on the basis of "one to many" classification, and the parameters are optimized and set. Finally, fault feature extraction method based on time-varying singular spectrum is applied to fault diagnosis of D122 reciprocating compressor. The diagnosis flow of reciprocating compressor is set up, including vibration acceleration signal acquisition, wavelet decomposition combined with LMD decomposition signal de-noising, time-varying singular spectrum extraction of fault feature vector and classification of fault type using SVM classifier. The classification results show that the accuracy of gas valve fault classification is 100 and bearing fault classification accuracy is 933. The validity of fault feature extraction based on time-varying singular spectrum method is verified. It shows that the feature vectors extracted by this method can accurately distinguish the main fault types of reciprocating compressors.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TH45

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