基于基追蹤、波形匹配和支持向量機的往復(fù)式壓縮機氣閥故障診斷研究
發(fā)布時間：2018-08-07 12:08
【摘要】：往復(fù)壓縮機廣泛應(yīng)用于石油和化工行業(yè),在生產(chǎn)線上往往屬于關(guān)鍵設(shè)備,其工作狀態(tài)的好壞直接影響著企業(yè)的經(jīng)濟效益。其結(jié)構(gòu)復(fù)雜且很多重要零部件工作在高溫高壓的惡劣環(huán)境下,并承受著往復(fù)載荷的作用,因而故障容易發(fā)生。與往復(fù)壓縮機中其它零部件相比,氣閥最脆弱,故障發(fā)生更頻繁。從故障診斷、事故預(yù)防、維修決策和節(jié)約成本的角度來說,研究有效準確的往復(fù)壓縮機氣閥故障診斷方法是非常重要和有意義的。 往復(fù)壓縮機的振動信號含有大量的周期成分和瞬態(tài)沖擊成分,有明顯的非平穩(wěn)特征。其各零部件,如活塞、連桿、氣閥等運動周期相同,頻率特征在頻譜上是重疊的,因此很難分辨。本文首先從往復(fù)壓縮機的運動機理出發(fā)建立了氣閥振動信號模型。然后,綜合信號處理和模式識別技術(shù),提出了一套新的往復(fù)壓縮機氣閥故障診斷方法。該方法結(jié)合了基追蹤,波形匹配和支持向量機三種算法,從時域上對氣閥狀態(tài)進行識別和故障診斷�；粉櫽脕硖崛≌駝有盘柕闹饕煞趾鸵种票尘霸肼�。波形匹配是本文提出的一種新的特征提取算法。傳統(tǒng)的特征提取算法大多數(shù)通過統(tǒng)計特征和熵等指標來提取特征。與此不同的是,波形匹配通過把振動波形和參數(shù)化波形做匹配來提取特征。匹配過程由差分進化算法來優(yōu)化。波形匹配提取的特征的維數(shù)小且各特征含有明確的物理意義。支持向量機適合處理小樣本問題,在本文中用來實現(xiàn)故障識別和分類。在理論介紹后,本文將提出的方法應(yīng)用于實驗數(shù)據(jù)和現(xiàn)場數(shù)據(jù)。實驗數(shù)據(jù)處理結(jié)果表明該診斷方法可以準確可靠地識別往復(fù)壓縮機氣閥的三種狀態(tài)(正常,彈簧惡化和閥片變形)。現(xiàn)場數(shù)據(jù)結(jié)果表明該診斷方法可以有效地識別氣閥故障程度。最后,本文對提出的新方法的優(yōu)點和局限性進行了討論,并且對在該方法基礎(chǔ)上的進一步研究進行了展望。
[Abstract]:Reciprocating compressors are widely used in petroleum and chemical industry, and often belong to the key equipment in the production line. The working state of reciprocating compressors directly affects the economic benefits of enterprises. Its structure is complex, and many important parts work in the harsh environment of high temperature and high pressure, and bear the function of reciprocating load, so the faults are easy to occur. Compared with other parts of reciprocating compressor, the valve is the most vulnerable and faults occur more frequently. From the point of view of fault diagnosis, accident prevention, maintenance decision and cost saving, it is very important and meaningful to study an effective and accurate fault diagnosis method for reciprocating compressor valve. The vibration signal of reciprocating compressor contains a large number of periodic components and transient shock components, and has obvious non-stationary characteristics. Its components, such as piston, connecting rod, valve and so on, have the same motion cycle, and the frequency characteristics overlap in frequency spectrum, so it is difficult to distinguish. In this paper, the vibration signal model of air valve is established based on the motion mechanism of reciprocating compressor. Then, a new fault diagnosis method for reciprocating compressor valve is proposed by integrating signal processing and pattern recognition techniques. This method combines three algorithms: base tracking, waveform matching and support vector machine to identify and diagnose the valve state in time domain. Base tracking is used to extract the main components of vibration signal and suppress background noise. Waveform matching is a new feature extraction algorithm proposed in this paper. Most of the traditional feature extraction algorithms use statistical features and entropy to extract features. In contrast, waveform matching extracts the feature by matching the vibration waveform with the parameterized waveform. The matching process is optimized by differential evolution algorithm. The dimension of the feature extracted by waveform matching is small and each feature has definite physical meaning. Support vector machine (SVM) is suitable for small sample problem and is used to realize fault identification and classification in this paper. After the introduction of the theory, the proposed method is applied to experimental data and field data. The experimental data show that the diagnostic method can accurately and reliably identify the three states of the reciprocating compressor valve (normal, spring deterioration and valve plate deformation). The field data show that the diagnosis method can effectively identify the fault degree of the valve. Finally, the advantages and limitations of the proposed new method are discussed, and the further research based on this method is prospected.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH45;TH165.3
本文編號：2169977
[Abstract]:Reciprocating compressors are widely used in petroleum and chemical industry, and often belong to the key equipment in the production line. The working state of reciprocating compressors directly affects the economic benefits of enterprises. Its structure is complex, and many important parts work in the harsh environment of high temperature and high pressure, and bear the function of reciprocating load, so the faults are easy to occur. Compared with other parts of reciprocating compressor, the valve is the most vulnerable and faults occur more frequently. From the point of view of fault diagnosis, accident prevention, maintenance decision and cost saving, it is very important and meaningful to study an effective and accurate fault diagnosis method for reciprocating compressor valve. The vibration signal of reciprocating compressor contains a large number of periodic components and transient shock components, and has obvious non-stationary characteristics. Its components, such as piston, connecting rod, valve and so on, have the same motion cycle, and the frequency characteristics overlap in frequency spectrum, so it is difficult to distinguish. In this paper, the vibration signal model of air valve is established based on the motion mechanism of reciprocating compressor. Then, a new fault diagnosis method for reciprocating compressor valve is proposed by integrating signal processing and pattern recognition techniques. This method combines three algorithms: base tracking, waveform matching and support vector machine to identify and diagnose the valve state in time domain. Base tracking is used to extract the main components of vibration signal and suppress background noise. Waveform matching is a new feature extraction algorithm proposed in this paper. Most of the traditional feature extraction algorithms use statistical features and entropy to extract features. In contrast, waveform matching extracts the feature by matching the vibration waveform with the parameterized waveform. The matching process is optimized by differential evolution algorithm. The dimension of the feature extracted by waveform matching is small and each feature has definite physical meaning. Support vector machine (SVM) is suitable for small sample problem and is used to realize fault identification and classification in this paper. After the introduction of the theory, the proposed method is applied to experimental data and field data. The experimental data show that the diagnostic method can accurately and reliably identify the three states of the reciprocating compressor valve (normal, spring deterioration and valve plate deformation). The field data show that the diagnosis method can effectively identify the fault degree of the valve. Finally, the advantages and limitations of the proposed new method are discussed, and the further research based on this method is prospected.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH45;TH165.3
【引證文獻】
相關(guān)期刊論文 前1條
1 董玉瓊;劉錦南;張藻平;;往復(fù)壓縮機氣閥故障的預(yù)知性維修[J];中國設(shè)備工程;2012年10期
，本文編號：2169977
本文鏈接：http://www.wukwdryxk.cn/kejilunwen/jixiegongcheng/2169977.html
最近更新
教材專著
