【摘要】：近幾年來(lái),隨著科學(xué)技術(shù)的不斷進(jìn)步與經(jīng)濟(jì)的不斷發(fā)展,各行業(yè)中的生產(chǎn)裝備都朝著大型化、精密化、復(fù)雜化、自動(dòng)化的方向發(fā)展。作為應(yīng)用最廣泛的旋轉(zhuǎn)類(lèi)機(jī)械部件,滾動(dòng)軸承直接決定并影響著整個(gè)系統(tǒng)的生產(chǎn)和運(yùn)行狀況。一方面,這些技術(shù)進(jìn)步能夠提升生產(chǎn)效率,為廠家?guī)?lái)可觀的生產(chǎn)效益和豐厚的利潤(rùn)回報(bào)；另一方面,裝備的大型化、復(fù)雜化、精密化及自動(dòng)化也極大提高了裝備的生產(chǎn)成本,一旦這些裝備發(fā)生故障,就會(huì)造成巨大的經(jīng)濟(jì)損失和人員傷亡事故。因此,對(duì)滾動(dòng)軸承故障模式識(shí)別技術(shù)展開(kāi)研究,保證其正常運(yùn)行,具有十分重要的意義。 本研究在國(guó)家“十一五”科技支撐計(jì)劃：“危險(xiǎn)化學(xué)品生產(chǎn)安全保障關(guān)鍵技術(shù)研究”(項(xiàng)目編號(hào)：2006BAK01B01)的支持下完成的,主要研究工作如下： 一、介紹了本課題的研究背景及目的,闡述了模式識(shí)別技術(shù)在國(guó)內(nèi)外的研究現(xiàn)狀及工程應(yīng)用,列舉了本研究的主要工作內(nèi)容及創(chuàng)新點(diǎn)。 二、介紹和研究了部分信號(hào)處理方法及特征選擇和提取技術(shù),主要包括快速傅里葉變換、循環(huán)統(tǒng)計(jì)理論、經(jīng)驗(yàn)?zāi)B(tài)分解以及基于奇異值分解和主成分分析的特征提取方法。 三、研究和改進(jìn)了本論文中的兩個(gè)重要模型,分別為經(jīng)驗(yàn)?zāi)B(tài)分解過(guò)程中的局部均值模型及端點(diǎn)效應(yīng)模型。在前人的研究基礎(chǔ)上,提出了極值域均值和極值間均值相結(jié)合的局部均值模型,研究了端點(diǎn)效應(yīng)處理方法,取得了一定的效果。 四、提出了基于二階循環(huán)統(tǒng)計(jì)量的奇異值分解模型的模式識(shí)別方法,并將其引入到滾動(dòng)軸承故障狀態(tài)識(shí)別中來(lái)。借助于CWRU軸承數(shù)據(jù)中心的滾動(dòng)軸承不同工作狀態(tài)數(shù)據(jù),對(duì)該模型進(jìn)行了實(shí)驗(yàn)驗(yàn)證,取得了較好的識(shí)別結(jié)果,可以值得深入研究和應(yīng)用。 五、提出了基于經(jīng)驗(yàn)?zāi)B(tài)分解的主成分分析模型的模式識(shí)別方法,并將其引入到滾動(dòng)軸承故障狀態(tài)識(shí)別中,在CWRU軸承數(shù)據(jù)中心的試驗(yàn)數(shù)據(jù)支持下,對(duì)該理論模型進(jìn)行了實(shí)驗(yàn)驗(yàn)證,結(jié)果表明識(shí)別精度較高,較好的完成了預(yù)期的目標(biāo)。
[Abstract]:In recent years, with the continuous progress of science and technology and the development of economy, the production equipment in various industries has developed towards the direction of large-scale, precision, complexity and automation. As the most widely used rotating mechanical parts, rolling bearings directly determine and affect the production and operation of the whole system. On the one hand, these technological advances can improve the efficiency of production, bring considerable production benefits and rich profit returns for manufacturers; On the other hand, the large-scale, complex, precision and automation of the equipment also greatly increase the production cost of the equipment, once these equipment failure, will cause huge economic losses and casualties. Therefore, it is of great significance to study the fault pattern recognition technology of rolling bearing to ensure its normal operation. This study was completed under the support of the National "Eleventh Five-Year Plan" Science and Technology support Plan: "Research on key Technologies for Safety and Security of Hazardous Chemicals production" (Project No.: 2006BAK01B01). The main research work is as follows: 1. This paper introduces the research background and purpose of this subject, expounds the research status and engineering application of pattern recognition technology at home and abroad, and lists the main work contents and innovation points of this research. Secondly, some signal processing methods and feature selection and extraction techniques are introduced and studied, including fast Fourier transform, cyclic statistical theory, empirical mode decomposition and feature extraction based on singular value decomposition and principal component analysis. Thirdly, two important models in this paper are studied and improved, namely, the local mean model and the endpoint effect model in the process of empirical mode decomposition. On the basis of previous studies, a local mean model combining the mean of polar range and the mean between extreme values is proposed, and the method to deal with the endpoint effect is studied, and some results are obtained. Fourthly, a pattern recognition method for singular value decomposition (SVD) model based on second-order cyclic statistics is proposed and applied to the fault state recognition of rolling bearings. With the help of the different working state data of the rolling bearing in the CWRU bearing data center, the model is verified by experiments and good recognition results are obtained, which is worthy of further study and application. 5. A method of principal component analysis (PCA) based on empirical mode decomposition (EMD) is proposed, which is applied to the fault state recognition of rolling bearing, supported by the experimental data of CWRU bearing data center. The experimental results show that the recognition accuracy is high and the expected target is achieved.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類(lèi)號(hào)】：TH165.3

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