【摘要】：近幾年來,隨著科學(xué)技術(shù)的不斷進步與經(jīng)濟的不斷發(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、自動化的方向發(fā)展。作為應(yīng)用最廣泛的旋轉(zhuǎn)類機械部件,滾動軸承直接決定并影響著整個系統(tǒng)的生產(chǎn)和運行狀況。一方面,這些技術(shù)進步能夠提升生產(chǎn)效率,為廠家?guī)砜捎^的生產(chǎn)效益和豐厚的利潤回報；另一方面,裝備的大型化、復(fù)雜化、精密化及自動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,就會造成巨大的經(jīng)濟損失和人員傷亡事故。因此,對滾動軸承故障模式識別技術(shù)展開研究,保證其正常運行,具有十分重要的意義。 本研究在國家“十一五”科技支撐計劃：“危險化學(xué)品生產(chǎn)安全保障關(guān)鍵技術(shù)研究”(項目編號：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,闡述了模式識別技術(shù)在國內(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。 二、介紹和研究了部分信號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)計理論、經(jīng)驗?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、研究和改進了本論文中的兩個重要模型,分別為經(jīng)驗?zāi)B(tài)分解過程中的局部均值模型及端點效應(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)計量的奇異值分解模型的模式識別方法,并將其引入到滾動軸承故障狀態(tài)識別中來。借助于CWRU軸承數(shù)據(jù)中心的滾動軸承不同工作狀態(tài)數(shù)據(jù),對該模型進行了實驗驗證,取得了較好的識別結(jié)果,可以值得深入研究和應(yīng)用。 五、提出了基于經(jīng)驗?zāi)B(tài)分解的主成分分析模型的模式識別方法,并將其引入到滾動軸承故障狀態(tài)識別中,在CWRU軸承數(shù)據(jù)中心的試驗數(shù)據(jù)支持下,對該理論模型進行了實驗驗證,結(jié)果表明識別精度較高,較好的完成了預(yù)期的目標。
[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)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

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