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

【摘要】：隨著中國工業(yè)化進程不斷推進,不斷有生產機器開始進入老化期,在將來會達到一個龐大的數(shù)量。滾動軸承是旋轉機械重要零部件之一,也是占比最大的故障源之一。因此,開展?jié)L動軸承故障診斷研究具有重要的現(xiàn)實意義和經濟意義。模態(tài)提取是滾動軸承故障診斷的關鍵,尤其是對滾動軸承故障特征的提取。滾動軸承振動信號屬于典型的非線性信號,特征提取的質量直接影響故障診斷結果。針對故障特征提取與識別問題,研究內容如下:(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的基本概念,敘述了如何構造變分模態(tài)分解方法中的信號約束問題,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)分解方法和經驗模態(tài)分解方法對噪聲干擾信號和脈沖干擾信號進行分解。結果表明,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)使用基于峭度準則VMD及平穩(wěn)小波的軸承故障診斷方法,提取強噪聲背景下的滾動軸承故障特征。首先使用變分模態(tài)分解對同一負荷下的故障信號進行預處理,再通過峭度準則篩選出最佳和次佳信號分量進行重構并使用平穩(wěn)小波進行去噪處理,最后分析信號的包絡譜來對軸承的故障類型進行判斷。通過對仿真滾動軸承內圈故障信號進行分析,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,基于峭度準則VMD及平穩(wěn)小波的軸承故障診斷可有效提取強聲背景下的滾動軸承早期故障信息,具有一定的可靠性和應用價值。(3)使用基于VMD瞬時能量法及MPSO-SVM的軸承故障診斷方法,實現(xiàn)軸承振動故障的較精確診斷。首先使用變分模態(tài)分解方法分解軸承振動信號,再根據(jù)VMD分量特性篩選出包含主要故障信息的分量進行瞬時能量特性計算并構建故障特征向量,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(Support Vector Machine,SVM)分類器中來區(qū)分滾動軸承的工作狀態(tài)和故障類型。對軸承正常狀態(tài)、內圈故障及外圈故障信號進行仿真實驗,該方法可較精確的對軸承振動信號進行故障分類,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.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133.33

【參考文獻】

相關期刊論文 前10條

1 錢林;康敏;傅秀清;王興盛;費秀國;;基于VMD的自適應形態(tài)學在軸承故障診斷中的應用[J];振動與沖擊;2017年03期

2 張淑清;黃文靜;胡永濤;宿新爽;陸超;姜萬錄;;基于總體平均經驗模式分解近似熵和混合PSO-BP算法的軸承故障診斷方法[J];中國機械工程;2016年22期

3 張東;馮志鵬;;基于變分模式分解和微積分增強能量算子的滾動軸承故障診斷[J];工程科學學報;2016年09期

4 黃璇;郭立紅;李姜;于洋;;改進粒子群優(yōu)化BP神經網(wǎng)絡的目標威脅估計[J];吉林大學學報(工學版);2017年03期

5 張亞超;劉開培;秦亮;;基于VMD-SE和機器學習算法的短期風電功率多層級綜合預測模型[J];電網(wǎng)技術;2016年05期

6 陳法法;楊勇;馬婧華;陳從平;;信息熵與優(yōu)化LS-SVM的軸承性能退化模糊�；A測[J];儀器儀表學報;2016年04期

7 姜久亮;劉文藝;侯玉潔;仲召明;陳思瑤;;基于內積延拓LMD及SVM的軸承故障診斷方法研究[J];振動與沖擊;2016年06期

8 石瑞敏;楊兆建;;基于LMD能量特征的滾動軸承故障診斷方法[J];振動.測試與診斷;2015年05期

9 向丹;岑健;;基于EMD熵特征融合的滾動軸承故障診斷方法[J];航空動力學報;2015年05期

10 劉長良;武英杰;甄成剛;;基于變分模態(tài)分解和模糊C均值聚類的滾動軸承故障診斷[J];中國電機工程學報;2015年13期

相關博士學位論文 前1條

1 程軍圣;基于Hilbert-Huang變換的旋轉機械故障診斷方法研究[D];湖南大學;2005年

相關碩士學位論文 前1條

1 王志武;強噪聲背景下機械故障微弱信號特征提取方法研究[D];中北大學;2014年

，

本文編號：1685839