本文選題：故障診斷　切入點(diǎn)：滾動(dòng)軸承　出處：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：隨著中國工業(yè)化進(jìn)程不斷推進(jìn),不斷有生產(chǎn)機(jī)器開始進(jìn)入老化期,在將來會(huì)達(dá)到一個(gè)龐大的數(shù)量。滾動(dòng)軸承是旋轉(zhuǎn)機(jī)械重要零部件之一,也是占比最大的故障源之一。因此,開展?jié)L動(dòng)軸承故障診斷研究具有重要的現(xiàn)實(shí)意義和經(jīng)濟(jì)意義。模態(tài)提取是滾動(dòng)軸承故障診斷的關(guān)鍵,尤其是對(duì)滾動(dòng)軸承故障特征的提取。滾動(dòng)軸承振動(dòng)信號(hào)屬于典型的非線性信號(hào),特征提取的質(zhì)量直接影響故障診斷結(jié)果。針對(duì)故障特征提取與識(shí)別問題,研究內(nèi)容如下:(1)通過介紹變分模態(tài)分解方法(Variational Mode Decomposition,VMD)中的本征模態(tài)函數(shù)、維納濾波和解析信號(hào)的基本概念,敘述了如何構(gòu)造變分模態(tài)分解方法中的信號(hào)約束問題,并隨后介紹了如何使用變分模態(tài)分解方法如何求解約束問題。為了驗(yàn)證變分模態(tài)分解方法的優(yōu)越性,分別用變分模態(tài)分解方法和經(jīng)驗(yàn)?zāi)B(tài)分解方法對(duì)噪聲干擾信號(hào)和脈沖干擾信號(hào)進(jìn)行分解。結(jié)果表明,變分模態(tài)方法在噪聲魯棒性和脈沖干擾性上具有明顯優(yōu)勢。(2)使用基于峭度準(zhǔn)則VMD及平穩(wěn)小波的軸承故障診斷方法,提取強(qiáng)噪聲背景下的滾動(dòng)軸承故障特征。首先使用變分模態(tài)分解對(duì)同一負(fù)荷下的故障信號(hào)進(jìn)行預(yù)處理,再通過峭度準(zhǔn)則篩選出最佳和次佳信號(hào)分量進(jìn)行重構(gòu)并使用平穩(wěn)小波進(jìn)行去噪處理,最后分析信號(hào)的包絡(luò)譜來對(duì)軸承的故障類型進(jìn)行判斷。通過對(duì)仿真滾動(dòng)軸承內(nèi)圈故障信號(hào)進(jìn)行分析,該方法可成功提取出微弱特征頻率信息,噪聲抑制效果優(yōu)于EMD(Empirical Mode Decomposition,EMD)。由此表明,基于峭度準(zhǔn)則VMD及平穩(wěn)小波的軸承故障診斷可有效提取強(qiáng)聲背景下的滾動(dòng)軸承早期故障信息,具有一定的可靠性和應(yīng)用價(jià)值。(3)使用基于VMD瞬時(shí)能量法及MPSO-SVM的軸承故障診斷方法,實(shí)現(xiàn)軸承振動(dòng)故障的較精確診斷。首先使用變分模態(tài)分解方法分解軸承振動(dòng)信號(hào),再根據(jù)VMD分量特性篩選出包含主要故障信息的分量進(jìn)行瞬時(shí)能量特性計(jì)算并構(gòu)建故障特征向量,最后將其輸入變異粒子群算法(Mutation Particle Swarm Optimization,MPSO)優(yōu)化后的支持向量機(jī)(Support Vector Machine,SVM)分類器中來區(qū)分滾動(dòng)軸承的工作狀態(tài)和故障類型。對(duì)軸承正常狀態(tài)、內(nèi)圈故障及外圈故障信號(hào)進(jìn)行仿真實(shí)驗(yàn),該方法可較精確的對(duì)軸承振動(dòng)信號(hào)進(jìn)行故障分類,具有良好的分類效果。
[Abstract]:As China's industrialization continues to advance, more and more production machines begin to enter the aging period, which will reach a large number in the future. Rolling bearings are one of the important parts of rotating machinery and one of the biggest fault sources. The research of rolling bearing fault diagnosis has important practical and economic significance. Modal extraction is the key of rolling bearing fault diagnosis. The vibration signal of rolling bearing is a typical nonlinear signal, and the quality of feature extraction directly affects the fault diagnosis result. The research contents are as follows: (1) by introducing the intrinsic mode functions, Wiener filtering and the basic concepts of analytical signals in the variational Mode decomposition method (VMD), the paper describes how to construct the signal constraint problem in the variational mode decomposition method. Then it introduces how to use variational mode decomposition method to solve constraint problem, in order to verify the superiority of variational mode decomposition method. The variational mode decomposition method and the empirical mode decomposition method are used to decompose the noise interference signal and the pulse interference signal respectively. Variational mode method has obvious advantages in noise robustness and impulse interference. (2) the bearing fault diagnosis method based on kurtosis criterion VMD and stationary wavelet is used. The fault characteristics of rolling bearing under strong noise background are extracted. Firstly, the fault signals under the same load are preprocessed by variational mode decomposition. Then the best and sub-optimal signal components are selected by kurtosis criterion for reconstruction, and the stationary wavelet is used to Denoise the signal. Finally, the envelope spectrum of the signal is analyzed to judge the fault type of the bearing. By analyzing the fault signal of the inner ring of the rolling bearing, the weak characteristic frequency information can be extracted successfully by this method. The noise suppression effect is better than that of EMD(Empirical Mode Decomposition.Therefore, it is shown that bearing fault diagnosis based on kurtosis criterion VMD and stationary wavelet can effectively extract the early fault information of rolling bearing under strong sound background. The method of bearing fault diagnosis based on VMD instantaneous energy method and MPSO-SVM is used to realize the accurate diagnosis of bearing vibration fault. First, the variational mode decomposition method is used to decompose the bearing vibration signal. Then according to the characteristics of VMD components, the components containing the main fault information are selected to calculate the instantaneous energy characteristics and the fault feature vectors are constructed. Finally, the support vector machine (SVM) support Vector Machine (SVM) classifier is used to distinguish the working state and fault type of rolling bearing by input mutation Particle Swarm optimization (MPSO). The simulation experiments are carried out on the normal state of bearing, inner ring fault and outer ring fault signal. This method can classify bearing vibration signals accurately and has good classification effect.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.33

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