發(fā)布時間：2018-04-01 01:08

  本文選題：磨削顫振　切入點：時變信號　出處：《浙江理工大學(xué)》2017年碩士論文

【摘要】：磨削加工是現(xiàn)代機械制造業(yè)中不可或缺的一種用來獲取高精度、低粗糙度的零件加工表面的工藝方法。對磨床進行狀態(tài)實時監(jiān)測和故障識別診斷來確保磨床長期穩(wěn)定可靠運行具有有重大現(xiàn)實意義和產(chǎn)業(yè)價值。需要注意的是,在加工過程中,磨床會進入顫振的狀態(tài),從而引發(fā)一系列負(fù)面影響。因此可靠的顫振監(jiān)測和識別技術(shù)是必不可少的,以實現(xiàn)磨床振動狀態(tài)的實時監(jiān)測。以傅里葉變換為理論基礎(chǔ)的傳統(tǒng)時頻信號處理方法不適用于非線性、非平穩(wěn)和多維的磨床振動輸出信號。二維經(jīng)驗?zāi)�態(tài)分解(Bivariate Empirical Mode Decomposition,BEMD)擴展了EMD的能力,能將二維復(fù)值信號分解為一系列零均值的旋轉(zhuǎn)成分。BEMD不僅能描述非線性動力學(xué)行為,而且能節(jié)約計算時間,并移除計算中由于假設(shè)和人為原因產(chǎn)生的失真。其在檢測初始故障方面表現(xiàn)出更強的能力,能有效地分析并提取非平穩(wěn)、非線性磨床顫振信號特征。本文以KD4020X16數(shù)控龍門導(dǎo)軌磨床為研究對象,根據(jù)磨床自身的動靜態(tài)特性搭建了顫振檢測試驗平臺,進行了磨削參數(shù)多水平試驗。利用IEPE壓電加速度傳感器和配套的TST5912動態(tài)信號分析儀對振動信號進行采集和保存,得到不同磨削參數(shù)設(shè)定下的80組實驗樣本數(shù)據(jù),其中包括45組平穩(wěn)磨削振動信號和35組顫振磨削信號。本論文對實驗過程中采集到的砂輪主軸X和Z方向的振動信號進行信號重構(gòu),進行BEMD處理得到多階BIMF分量;利用基于相關(guān)系數(shù)的真實固有模態(tài)函數(shù)提取準(zhǔn)則篩選出真實BIMF;提取出對顫振信號敏感的指標(biāo)量—峰峰值、實時方差、峭度以及瞬時能量,分別進行求和與歸一化處理形成顫振特征向量;最后以最小二乘支持向量機作為(Least Square Support Vector Machine,LSSVM)智能化模式分類器對隨機選取的55組樣本數(shù)據(jù)的特征量進行訓(xùn)練,得到顫振檢測識別模型,以剩下的25組樣本數(shù)據(jù)作為檢驗樣本,對識別模型進行檢驗和判斷,驗證其準(zhǔn)確率及可行性。證明了基于BEMD與LSSVM的方法具有較好的識別率。通過上述方法,建立了大型數(shù)控磨床磨削顫振檢測軟件,驗證了其實時監(jiān)測磨床振動狀態(tài)的可行性。
[Abstract]:Grinding is an indispensable method in modern mechanical manufacturing industry to obtain high precision. Process method for machining surface of parts with low roughness. It is of great practical significance and industrial value to ensure the long-term stable and reliable operation of grinding machine by real-time monitoring and fault identification diagnosis of grinding machine. Grinding machines enter a flutter state, causing a series of negative effects. Reliable flutter monitoring and identification techniques are therefore essential. In order to realize the real-time monitoring of grinding machine vibration, the traditional time-frequency signal processing method based on Fourier transform theory is not suitable for nonlinear. The two-dimensional empirical mode decomposition extends the ability of EMD to decompose the two-dimensional complex signal into a series of rotating components with zero mean value. BEMD can not only describe the nonlinear dynamic behavior. Moreover, it can save calculation time and remove the distortion caused by assumptions and human causes. It has a stronger ability to detect initial faults and can effectively analyze and extract non-stationary. Based on the dynamic and static characteristics of the KD4020X16 CNC gantry guideway grinder, a flutter detection test platform is built in this paper, which is based on the dynamic and static characteristics of the grinder itself. The vibration signals were collected and saved by IEPE piezoelectric accelerometer and TST5912 dynamic signal analyzer, and 80 sets of experimental data were obtained under different grinding parameters. This paper reconstructs the vibration signals in X and Z directions of the grinding wheel spindle collected during the experiment, and obtains the multi-order BIMF component by BEMD processing, which includes 45 sets of stationary grinding vibration signals and 35 sets of chatter grinding signals. The real BIMF is selected by using the real inherent mode function extraction criterion based on correlation coefficient, and the peak peak value, real time variance, kurtosis and instantaneous energy sensitive to flutter signal are extracted. Finally, the least square support vector machine is used as the intelligent pattern classifier for least Square Support Vector machine to train the characteristic quantity of 55 groups of randomly selected sample data. The flutter detection and identification model is obtained. The remaining 25 sets of sample data are used as test samples to test and judge the identification model. The accuracy and feasibility of the method are verified. The method based on BEMD and LSSVM has a good recognition rate. Through the above method, a large NC grinding machine grinding chatter detection software is established, and the feasibility of real-time monitoring the vibration state of grinding machine is verified.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG580.6

【參考文獻】

相關(guān)期刊論文 前10條

1 張飛;葛新峰;潘羅平;付婧;;穩(wěn)態(tài)工況下水電機組主軸擺度峰峰值計算方法研究[J];振動與沖擊;2015年21期

2 岳曉峰;邵海賀;;基于相似極值延拓的EMD端點效應(yīng)改進方法[J];組合機床與自動化加工技術(shù);2015年09期

3 王民;劉國付;昝濤;姚子良;;基于控制圖的磨削顫振預(yù)測方法[J];北京工業(yè)大學(xué)學(xué)報;2015年09期

4 鄭近德;程軍圣;曾鳴;羅頌榮;;廣義經(jīng)驗?zāi)�態(tài)分解性能分析與應(yīng)用[J];振動與沖擊;2015年03期

5 時培明;蔣金水;劉彬;王俊;;基于邊界特征尺度匹配延拓的EMD改進方法及應(yīng)用[J];中國機械工程;2014年12期

6 姚亞夫;張星;;基于瞬時能量熵和SVM的滾動軸承故障診斷[J];電子測量與儀器學(xué)報;2013年10期

7 鄭勝峰;陳素明;狄金海;王本軼;;一種基于多重互相關(guān)的相位差測量新方法[J];宇航計測技術(shù);2012年01期

8 毋文峰;陳小虎;蘇勛家;王旭平;姚春江;;基于峭度的ICA特征提取和齒輪泵故障診斷[J];機械科學(xué)與技術(shù);2011年09期

9 張俊;黎明;魯宇明;楊小芹;;基于BEMD的疲勞斷口圖像分割[J];失效分析與預(yù)防;2011年02期

10 韓建平;錢炯;董小軍;;采用鏡像延拓和RBF神經(jīng)網(wǎng)絡(luò)處理EMD中端點效應(yīng)[J];振動.測試與診斷;2010年04期

，

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