發(fā)布時(shí)間：2018-05-06 07:55

  本文選題：往復(fù)壓縮機(jī) + 故障診斷��； 參考：《東北石油大學(xué)》2015年碩士論文

【摘要】：往復(fù)壓縮機(jī)是用于壓縮和輸送氣體的機(jī)械設(shè)備,已成為石油化工領(lǐng)域必不可少的關(guān)鍵設(shè)備,隨著社會(huì)對(duì)安全生產(chǎn)重視程度的日益提高,對(duì)其實(shí)施故障診斷已勢在必行。然而,往復(fù)壓縮機(jī)結(jié)構(gòu)復(fù)雜,激勵(lì)源眾多,其故障診斷過程仍存在諸多問題亟待深入研究。本文以往復(fù)壓縮機(jī)振動(dòng)信號(hào)為信息源,針對(duì)信號(hào)所具有的非線性、強(qiáng)非平穩(wěn)特性,進(jìn)行基于局部特征尺度分解(LCD)和多尺度熵(MSE)的特征提取方法研究。首先,在對(duì)往復(fù)壓縮機(jī)常見故障形式進(jìn)行調(diào)研以及故障機(jī)理進(jìn)行深入分析的基礎(chǔ)上,確定以往復(fù)壓縮機(jī)氣缸與傳動(dòng)機(jī)構(gòu)部位的常見故障為研究對(duì)象,以振動(dòng)信號(hào)分析為手段,開展其故障診斷方法研究。以2D12型往復(fù)壓縮機(jī)為具體實(shí)驗(yàn)對(duì)象,進(jìn)行故障模擬實(shí)驗(yàn)并測試狀態(tài)數(shù)據(jù),為故障診斷過程實(shí)施奠定了基礎(chǔ)。其次,針對(duì)往復(fù)壓縮機(jī)振動(dòng)信號(hào)的非平穩(wěn)特性,使用LCD時(shí)頻分析方法對(duì)振動(dòng)信號(hào)進(jìn)行自適應(yīng)分解,突出了狀態(tài)信息。并針對(duì)LCD方法在分解強(qiáng)非平穩(wěn)信號(hào)時(shí),基線出現(xiàn)的波形毛刺失真現(xiàn)象,提出了以三次Hermite插值代替線性變換的基線構(gòu)造方法,對(duì)LCD進(jìn)行了改進(jìn)。通過對(duì)仿真信號(hào)和實(shí)測信號(hào)的對(duì)比分析,驗(yàn)證了該方法的有效性。再者,針對(duì)往復(fù)壓縮機(jī)振動(dòng)信號(hào)的非線性特性,提出了基于LCD和MSE的故障特征提取方法。利用相關(guān)系數(shù)法篩選了LCD分解結(jié)果中包含主要狀態(tài)信息的ISC分量,采用MSE對(duì)ISC分量進(jìn)行了復(fù)雜性定量描述,并以奇異值分解方法提取了狀態(tài)特征矩陣的特征值,作為最終的狀態(tài)特征向量。最后,以往復(fù)壓縮機(jī)的氣缸與傳動(dòng)機(jī)構(gòu)部位的常見故障形式為對(duì)象,利用基于LCD和MSE的故障特征提取方法實(shí)現(xiàn)了狀態(tài)特征的有效提取,并以支持向量機(jī)(SVM)為模式識(shí)別器,實(shí)現(xiàn)了故障的準(zhǔn)確診斷,通過與多種特征提取方法對(duì)別,驗(yàn)證了本文方法的優(yōu)越性。
[Abstract]:Reciprocating compressor is a kind of mechanical equipment used to compress and transport gas. It has become an indispensable key equipment in petrochemical field. With the increasing attention to safety in production, it is imperative to implement fault diagnosis of reciprocating compressor. However, the reciprocating compressor has a complex structure and a large number of excitation sources, so there are still many problems in the fault diagnosis of reciprocating compressor. In this paper, based on the local characteristic scale decomposition (LCD) and multi-scale entropy (MSE), the method of feature extraction based on local characteristic scale decomposition (LCD) and multi-scale entropy (MSE) is studied in view of the nonlinear and strong non-stationary characteristics of reciprocating compressor vibration signal as information source. First of all, on the basis of the investigation of the common fault forms of reciprocating compressor and the analysis of the fault mechanism, it is determined that the common faults in the cylinder and transmission mechanism of reciprocating compressor are taken as the research object, and the vibration signal analysis is taken as the means. The method of fault diagnosis is studied. Taking the 2D12 reciprocating compressor as the concrete experiment object, the fault simulation experiment and the test state data were carried out, which laid the foundation for the implementation of the fault diagnosis process. Secondly, according to the non-stationary characteristic of reciprocating compressor vibration signal, the LCD time-frequency analysis method is used to decompose the vibration signal adaptively, which highlights the state information. Aiming at the waveform burr distortion of LCD method when decomposing strong non-stationary signals, a method of constructing baselines with cubic Hermite interpolation instead of linear transformation is proposed, and the LCD is improved. The effectiveness of the method is verified by comparing the simulated signal with the measured signal. Furthermore, a fault feature extraction method based on LCD and MSE is proposed for the nonlinear characteristics of reciprocating compressor vibration signals. The correlation coefficient method is used to screen the ISC components containing the main state information in the LCD decomposition results, the complexity of the ISC components is quantitatively described by MSE, and the eigenvalues of the state characteristic matrices are extracted by the singular value decomposition method. As the final state eigenvector. Finally, taking the common fault form of cylinder and transmission mechanism of reciprocating compressor as the object, the effective state feature extraction method based on LCD and MSE is used, and the support vector machine (SVM) is used as the pattern recognizer. The fault diagnosis is realized, and the superiority of this method is verified by distinguishing with various feature extraction methods.
【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】：2015
【分類號(hào)】：TE964

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王正洪,尤一匡,張小鳴,張琳;往復(fù)壓縮機(jī)的狀態(tài)監(jiān)測和故障的診斷方法[J];江蘇工業(yè)學(xué)院學(xué)報(bào);2004年03期

2 孫明;往復(fù)壓縮機(jī)故障診斷綜述[J];油氣田地面工程;2004年08期

3 張琳,朱瑞松,尤一匡,尤侯平,王正洪;往復(fù)壓縮機(jī)監(jiān)測與診斷技術(shù)研究現(xiàn)狀與展望[J];化工進(jìn)展;2004年10期

4 戴新西;賀運(yùn)初;王大勇;;大型往復(fù)壓縮機(jī)異常振動(dòng)原因分析及處理[J];石油和化工設(shè)備;2006年03期

5 趙杰;李峰;劉錄;車俊鐵;韓麗艷;;超高壓往復(fù)壓縮機(jī)機(jī)體振動(dòng)分析[J];壓縮機(jī)技術(shù);2008年01期

6 謝紅杰;郝點(diǎn);任志安;;往復(fù)壓縮機(jī)軸承的振動(dòng)測試分析[J];貴州化工;2008年05期

7 李輝;;往復(fù)壓縮機(jī)故障的分析和排除[J];石油化工自動(dòng)化;2009年01期

8 唐友福;劉樹林;劉穎慧;姜銳紅;;基于α穩(wěn)定分布的往復(fù)壓縮機(jī)故障診斷方法[J];化工機(jī)械;2011年05期

9 丁磊;;往復(fù)壓縮機(jī)管道的減振設(shè)計(jì)[J];化工設(shè)計(jì);2012年03期

10 侯偉平;陳亮;;往復(fù)壓縮機(jī)脈動(dòng)及振動(dòng)控制淺探[J];中國石油和化工標(biāo)準(zhǔn)與質(zhì)量;2012年07期

相關(guān)會(huì)議論文 前8條

1 王金東;王巍;李宏?duì)N;李新偉;;往復(fù)壓縮機(jī)軸承故障的多重分形特征提取[A];2008年全國振動(dòng)工程及應(yīng)用學(xué)術(shù)會(huì)議暨第十一屆全國設(shè)備故障診斷學(xué)術(shù)會(huì)議論文集[C];2008年

2 王智偉;張鵬;王金東;杜永軍;王巍;;基于虛擬儀器的往復(fù)壓縮機(jī)監(jiān)測系統(tǒng)研究[A];第十二屆全國設(shè)備故障診斷學(xué)術(shù)會(huì)議論文集[C];2010年

3 丁月敏;;探究往復(fù)壓縮機(jī)的設(shè)計(jì)方案 以4M80(85)為例[A];第八屆沈陽科學(xué)學(xué)術(shù)年會(huì)論文集[C];2011年

4 郭文濤;;往復(fù)壓縮機(jī)管線的振動(dòng)分析方法探究[A];創(chuàng)新沈陽文集（A）[C];2009年

5 竇唯;孫明;劉樹林;;基于免疫機(jī)理的往復(fù)壓縮機(jī)在線狀態(tài)監(jiān)測方法[A];第八屆全國振動(dòng)理論及應(yīng)用學(xué)術(shù)會(huì)議論文集摘要[C];2003年

6 趙海峰;劉樹林;王憲明;徐艷;;往復(fù)壓縮機(jī)氣閥故障的瞬態(tài)特征提取方法研究[A];2008年全國振動(dòng)工程及應(yīng)用學(xué)術(shù)會(huì)議暨第十一屆全國設(shè)備故障診斷學(xué)術(shù)會(huì)議論文集[C];2008年

7 姚德群;余志紅;楊素春;王朝暉;;基于數(shù)據(jù)挖掘的往復(fù)壓縮機(jī)氣閥故障診斷設(shè)計(jì)[A];設(shè)備監(jiān)測與診斷技術(shù)及其應(yīng)用——第十二屆全國設(shè)備監(jiān)測與診斷學(xué)術(shù)會(huì)議論文集[C];2005年

8 高晶波;王日新;徐敏強(qiáng);夏松波;;基于時(shí)頻分析的往復(fù)壓縮機(jī)氣閥故障診斷方法的研究[A];第八屆全國振動(dòng)理論及應(yīng)用學(xué)術(shù)會(huì)議論文集摘要[C];2003年

相關(guān)博士學(xué)位論文 前2條

1 趙海洋;往復(fù)壓縮機(jī)軸承間隙故障診斷與狀態(tài)評(píng)估方法研究[D];哈爾濱工業(yè)大學(xué);2014年

2 趙海峰;基于HHT的往復(fù)壓縮機(jī)故障診斷研究[D];東北石油大學(xué);2011年

相關(guān)碩士學(xué)位論文 前10條

1 臧志賓;往復(fù)壓縮機(jī)潤滑站系統(tǒng)結(jié)構(gòu)研究及優(yōu)化設(shè)計(jì)[D];沈陽理工大學(xué);2015年

2 于小龍;基于模態(tài)分析的往復(fù)壓縮機(jī)管線故障診斷及減振方法研究[D];北京化工大學(xué);2015年

3 伍永強(qiáng);往復(fù)壓縮機(jī)網(wǎng)狀簧片閥工作特性研究[D];廣西大學(xué);2015年

4 徐豐甜;基于敏感特征提取的往復(fù)壓縮機(jī)智能診斷技術(shù)研究[D];北京化工大學(xué);2015年

5 趙雨薇;往復(fù)壓縮機(jī)早期故障智能預(yù)警關(guān)鍵技術(shù)研究及應(yīng)用[D];北京化工大學(xué);2015年

6 謝軼男;往復(fù)壓縮機(jī)氣閥瞬態(tài)特性及故障模擬研究[D];北京化工大學(xué);2015年

7 田，

本文編號(hào)：1851443