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  本文關鍵詞：基于全矢譜的旋轉機械故障特征提取研究　出處：《鄭州大學》2011年碩士論文　論文類型：學位論文

  更多相關文章： 全矢譜 特征提取 粗糙集 小波-包絡分析 全矢小波分析 故障診斷

【摘要】：大型旋轉類機械往往是企業(yè)的關鍵性咽喉設備,它們以轉子及其它回轉部件作為工作的主體。在石油、化工、冶金、發(fā)電等大、中型企業(yè)中,旋轉設備約占80%的比例,包括壓縮機、鼓風機、汽輪機、發(fā)電機、軋鋼機等。作為企業(yè)的核心設備,一旦發(fā)生故障,將給企業(yè)甚至人們的生命財產(chǎn)帶來難以估量的損失和傷害。所以大型旋轉類設備運行狀態(tài)的監(jiān)測及其故障的及時診斷和解決也是科技工作者愈來愈關心的問題。 旋轉類機械運行狀態(tài)的監(jiān)測及其故障診斷依據(jù)是被診斷對象所表征的一切有用的信息,比如振動、噪聲、轉速、溫度、壓力、流量等。旋轉類機械的振動信號中蘊含了大量的信息,可以幫助人們監(jiān)測設備的運行狀態(tài)及判斷故障的類型。故障特征提取就是對系統(tǒng)的動態(tài)信號預處理后得到的信息進行分析和處理,提取與系統(tǒng)狀態(tài)有關的數(shù)據(jù),再對得到的數(shù)據(jù)進行處理和分析,提取其中與系統(tǒng)狀態(tài)相關性較大的敏感特征。有效特征向量的提取是故障診斷中的關鍵環(huán)節(jié),也是能否及時、正確的做出故障診斷的關鍵因素。 針對傳統(tǒng)單通道信息采集的不完整及實時性差等問題,本文將全矢譜技術分別與粗集理論、小波分析方法結合起來,提出了基于全矢譜技術的旋轉類機械故障特征提取方法。 全矢譜分析技術基于旋轉機械同源信息融合,它是矢量譜分析及其一系列擴展分析方法的統(tǒng)稱。它可以融合轉子一個截面上的兩個或三個通道的信息并對這些信息進行組合,它不僅彌補了傳統(tǒng)單通道分析信息不足及不完整等缺點,而且具有分辨率高、三維分析可行性、高分辨率下指示轉子在各回轉頻率下的振動強度和方位及與傳統(tǒng)分析方法的兼容性等特點。 粗糙集理論是由波蘭的Z.Pawlak教授于上世紀80年代提出,是一種能夠分析處理不精確、不一致、不完整信息與知識的數(shù)學工具,它的基本思想是通過數(shù)據(jù)庫分類歸納形成概念和規(guī)則,通過等價關系的分類對目標的近似實現(xiàn)知識發(fā)現(xiàn)。通常用來作為數(shù)據(jù)約簡的工具,它在消除冗余信息等方面有良好效果。小波分析是傅里葉分析的一個自然延伸,小波分析技術是由Morlet在1984年首先提出的,它克服了傳統(tǒng)傅里葉變換只考慮正弦振動的能量而沒有考慮其他振動方式的能量的缺點,對輸入信號的要求較低,具有靈敏度高,克服噪聲能力強等優(yōu)點。小波變換具有良好的時頻局部化特性和對信號自適應變焦、多分辨率分析的能力,可以將信號在不同尺度上展開.提取各個頻帶上的特性的同時也保留了頻帶相應的各個尺度上的時頻特性。用小波分析技術對故障特征進行提取更為有效。 本文探討了矢譜理論的基本原理及算法,并將全矢譜技術分別與粗糙集理論和小波分析技術相結合,提出了基于全矢譜技術-粗集理論在旋轉機械頻譜特征提取中的應用及對小波-包絡分析和全矢小波分析在滾動軸承故障特征提取中應用并對兩種方法進行對比研究,編制Matlab程序及進行相關實驗驗證其功能。
[Abstract]:Large rotating machinery is often the key equipments in the enterprise, they are the rotor and other rotating parts as the main body of work. In the petroleum, chemical, metallurgy, power generation and other large and medium-sized enterprises, accounting for about 80% of the proportion of rotating equipment, including compressor, blower, steam turbine, generator, rolling machines as the core. The equipment of enterprises, once the fault will bring immeasurable loss and damage to the enterprise and even the life and property of people. So the monitoring of large rotating equipment operating status and fault diagnosis and solution is more and more concerned about the problems of science and technology workers.
Monitoring and fault diagnosis for rotating machinery running condition is diagnosed all the useful information, object representation such as vibration, noise, speed, temperature, pressure, flow and so on. The vibration signal contains a lot of information, can help people to monitor the operational status of equipment and determine the fault type. Analysis and treatment of fault feature extraction is the system dynamic signal after pretreatment of information extraction, data related to the system state, then the processing and analysis of the data obtained from the system state and correlated sensitive features. The feature vector is the key of fault diagnosis, but also timely, correctly make fault diagnosis of the key factors.
Aiming at the problems of incomplete information and poor real-time performance of traditional single channel information collection, the full vector spectrum technology is combined with rough set theory and wavelet analysis method respectively. A feature extraction method of rotating machinery based on full vector spectrum technology is proposed.
The full vector spectrum analysis technology of rotating machinery based on homologous information fusion, it is referred to as the vector spectrum analysis and patulous analysis methods. It can be two or three channel fusion rotor to a section of the information and the combination of these information, it not only makes up for the traditional single channel analysis and the lack of information is not complete shortcomings, but also has high resolution, three-dimensional feasibility analysis, compatibility features of instruction of the rotor's vibration intensity and range and the traditional analysis method of high resolution.
Rough set theory is put forward by Professor Z.Pawlak of Poland in 80s, is an analysis to deal with imprecise, inconsistent, incomplete mathematical tools of information and knowledge, it is the basic idea of the database classification form concepts and rules, similar to realize knowledge discovery of target classification by equivalence relations. Usually used as the tools of data reduction, it has good effect in eliminating redundant information. Wavelet analysis is an extension of Fourier analysis, wavelet analysis is first proposed by Morlet in 1984, which overcomes the disadvantages of traditional Fourier transform only considering sinusoidal vibration energy without considering other modes of vibration energy faults, low requirement on the input signal, which has high sensitivity, strong ability to overcome the advantages of noise. Wavelet transform has good time-frequency localization characteristics and the signal from Adapting to the capability of zoom and multiresolution analysis, we can expand the signal on different scales, extract the characteristics of each frequency band, and also retain the time-frequency characteristics of the corresponding scales at different frequencies. It is more effective to extract fault features with wavelet analysis technology.
This paper discusses the basic principle and algorithm of vector spectrum theory, and the vector spectrum theory and wavelet analysis technology respectively with the combination of rough sets, puts forward the application of the full vector spectrum technology of rough set theory in rotating machinery spectrum in the feature extraction and wavelet envelope analysis and full vector wavelet analysis and comparative study the two methods used in rolling bearing fault feature extraction based on Matlab program and the related experiments to verify its function.

【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【引證文獻】

相關碩士學位論文 前2條

1 尚慧娟;面向全矢譜分析的轉子動態(tài)故障特性研究[D];鄭州大學;2012年

2 王東方;面向云計算的設備故障診斷系統(tǒng)關鍵技術研究[D];鄭州大學;2012年

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本文編號：1373435