本文選題：掘進(jìn)機(jī)截割機(jī)構(gòu) + 逆虛擬激勵(lì)法　； 參考：《中北大學(xué)》2017年碩士論文

【摘要】：掘進(jìn)機(jī)在高速重載條件下工作,其良好的性能是煤礦安全生產(chǎn)的保障,而其安全性與截割機(jī)構(gòu)的運(yùn)行狀態(tài)有很大關(guān)系。通過對掘進(jìn)機(jī)截割機(jī)構(gòu)的載荷分析研究,及時(shí)精確識(shí)別其承受動(dòng)態(tài)載荷,能夠?yàn)楣收项A(yù)示和演化、壽命預(yù)測等提供技術(shù)支持。然而截割機(jī)構(gòu)的載荷通常又無法直接測試得到,因此就有必要通過間接手段求解其載荷,載荷識(shí)別技術(shù)是一種非常有效的方法。本文首先對載荷識(shí)別技術(shù)進(jìn)行了研究,載荷識(shí)別技術(shù)包括頻域法、時(shí)域法和人工智能方法,由于掘進(jìn)機(jī)截割機(jī)構(gòu)受到的是隨機(jī)載荷,隨機(jī)載荷識(shí)別一般采用頻域法,而其中的逆虛擬激勵(lì)法計(jì)算簡單,精度也較高,因此本文通過逆虛擬激勵(lì)法對掘進(jìn)機(jī)截割機(jī)構(gòu)載荷進(jìn)行識(shí)別。逆虛擬激勵(lì)法需要得知系統(tǒng)的頻響函數(shù)逆矩陣,鑒于頻響函數(shù)求逆存在病態(tài)問題,本文引入改進(jìn)Tikhonov正則化法來改善識(shí)別效果,用GCV準(zhǔn)則來求解正則參數(shù),并用該方法做了懸臂梁的MATLAB仿真,得出其可以將載荷識(shí)別誤差降低2.3dB,在改善精度方面較之前方法有較理想的效果。本文通過實(shí)驗(yàn)驗(yàn)證了該方法的可行性。由于礦井中的響應(yīng)測試環(huán)境比較復(fù)雜,很難順利和高效地進(jìn)行測試振動(dòng)信號(hào)的工作,因而本文搭建即地面假巖壁的載荷試驗(yàn)平臺(tái),在井上進(jìn)行了切割假巖壁的試驗(yàn)。要測得掘進(jìn)機(jī)截割機(jī)構(gòu)上的振動(dòng)響應(yīng),必先須對傳感器進(jìn)行優(yōu)化布置。本文根據(jù)模態(tài)置信準(zhǔn)則建立了傳感器的適應(yīng)度函數(shù),利用粒子群優(yōu)化算法求解其適應(yīng)度值作為評價(jià)傳感器組合優(yōu)劣的依據(jù),得出了傳感器的優(yōu)化布置方式,進(jìn)而測得了截割機(jī)構(gòu)的振動(dòng)響應(yīng)。此外還需要獲得掘進(jìn)機(jī)截割機(jī)構(gòu)的頻響函數(shù),本文通過ANSYS中進(jìn)行瞬態(tài)分析求得頻響函數(shù)。在掘進(jìn)機(jī)截割機(jī)構(gòu)的有限元模型上根據(jù)測點(diǎn)布置原則選擇激勵(lì)點(diǎn)施加載荷,根據(jù)瞬態(tài)動(dòng)力學(xué)分析獲得了截割機(jī)構(gòu)上響應(yīng)測點(diǎn)的位移信號(hào)。在MATLAB中寫入載荷,和測點(diǎn)位移,分別對它們作傅里葉變換,然后據(jù)此求出系統(tǒng)的頻響函數(shù)。獲得其頻響函數(shù)后,由改進(jìn)正則化法求解其廣義逆矩陣。然后采用逆虛擬激勵(lì)法,通過實(shí)測振動(dòng)信號(hào)建立響應(yīng)功率譜矩陣,構(gòu)造虛擬響應(yīng)向量,求解各虛擬激勵(lì)向量,合成載荷功率譜矩陣。通過與由實(shí)際測得的應(yīng)變求得截割機(jī)構(gòu)的載荷功率譜對比,得出該方法可以在非固有頻率頻段內(nèi)和固有頻率頻段內(nèi)分別將載荷識(shí)別誤差最大降低2.23dB和1.44dB。因此該方法可以提升載荷識(shí)別的精度,為更深入地研究掘進(jìn)機(jī)的力學(xué)特性及對其進(jìn)行優(yōu)化設(shè)計(jì)提供了依據(jù)。
[Abstract]:The good performance of roadheader working under the condition of high speed and heavy load is the guarantee of coal mine safety production, and its safety is closely related to the running state of cutting mechanism. By analyzing and studying the load of the cutting mechanism of the roadheader, the dynamic load can be recognized accurately and timely, which can provide technical support for fault prediction and evolution, life prediction and so on. However, the load of cutting mechanism can not be measured directly, so it is necessary to solve the load by indirect means. Load identification is a very effective method. In this paper, the technology of load identification is studied firstly. The technology of load identification includes frequency domain method, time domain method and artificial intelligence method. Because the cutting mechanism of roadheader is subjected to random load, the random load identification generally adopts frequency domain method. The inverse virtual excitation method is simple in calculation and high in accuracy, so this paper uses the inverse virtual excitation method to identify the load of the cutting mechanism of the tunneling machine. The inverse virtual excitation method needs to know the inverse matrix of the frequency response function of the system. In view of the ill-posed problem of finding the inverse of the frequency response function, the improved Tikhonov regularization method is introduced to improve the recognition effect and the GCV criterion is used to solve the regular parameters. The method is used to simulate the cantilever beam by MATLAB, and it is concluded that the load identification error can be reduced by 2.3 dB, and the accuracy of the method is better than that of the previous method. The feasibility of this method is verified by experiments in this paper. Because the response testing environment in mine is complex, it is difficult to test vibration signals smoothly and efficiently. Therefore, the loading test platform of surface pseudo rock wall is set up in this paper, and the test of cutting false rock wall is carried out in the well. In order to measure the vibration response of the cutting mechanism of the roadheader, the sensor must be arranged optimally. In this paper, the fitness function of the sensor is established according to the modal confidence criterion. The particle swarm optimization algorithm is used to solve its fitness value as the basis for evaluating the combination of sensors, and the optimal arrangement of the sensor is obtained. Then the vibration response of the cutting mechanism is measured. In addition, the frequency response function of the cutting mechanism of roadheader is also needed. The frequency response function is obtained by transient analysis in ANSYS. In the finite element model of the cutting mechanism of the roadheader, the excitation point is selected to apply load according to the principle of measuring point arrangement, and the displacement signal of the response point on the cutting mechanism is obtained according to the transient dynamic analysis. The load and displacement of measuring points are written in MATLAB. The Fourier transform is performed on them, and then the frequency response function of the system is obtained. After the frequency response function is obtained, the generalized inverse matrix is solved by the improved regularization method. Then the response power spectrum matrix is established by using the inverse virtual excitation method and the response power spectrum matrix is established by the measured vibration signal. The virtual response vector is constructed to solve each virtual excitation vector and the load power spectrum matrix is synthesized. Compared with the actual measured strain, the load power spectrum of the cutting mechanism is obtained. It is concluded that this method can reduce the maximum load identification error in the frequency band of the non-natural frequency and the frequency band of the natural frequency, respectively, and reduce the 2.23dB and 1.44 dB respectively. Therefore, this method can improve the accuracy of load identification, and provide a basis for further research on mechanical characteristics and optimization design of roadheader.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD421.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 姜鑫;張方;姜金輝;;基于模態(tài)坐標(biāo)變換梁分布動(dòng)載荷識(shí)別技術(shù)[J];國外電子測量技術(shù);2016年02期

2 襲著有;閆云聚;常曉通;;基于遺傳算法的動(dòng)態(tài)載荷識(shí)別優(yōu)化方法[J];機(jī)械強(qiáng)度;2015年04期

3 馬超;華宏星;;一種基于新的正則化技術(shù)的沖擊載荷識(shí)別法[J];振動(dòng)與沖擊;2015年12期

4 楊智春;賈有;;動(dòng)載荷識(shí)別方法的研究進(jìn)展[J];力學(xué)與實(shí)踐;2015年01期

5 張夏陽;黃其青;殷之平;曹善成;劉飛;;基于GA-ELM的飛行載荷參數(shù)識(shí)別[J];航空工程進(jìn)展;2014年04期

6 廖俊;蔣炳炎;時(shí)偉;;一種提高平穩(wěn)隨機(jī)載荷識(shí)別精度的方法[J];振動(dòng)與沖擊;2013年16期

7 魏曉華;師建國;;縱軸式掘進(jìn)機(jī)截割過程動(dòng)力學(xué)建模與仿真[J];遼寧工程技術(shù)大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年01期

8 宗周紅;孫建林;徐立群;李嘉維;;大跨度連續(xù)剛構(gòu)橋健康監(jiān)測加速度傳感器優(yōu)化布置研究[J];地震工程與工程振動(dòng);2009年02期

9 潘宏俠;黃晉英;毛鴻偉;劉振旺;;基于粒子群優(yōu)化的故障特征提取技術(shù)研究[J];振動(dòng)與沖擊;2008年10期

10 李東升,李宏男,郭杏林;廣義小量分解法在載荷識(shí)別中的應(yīng)用[J];振動(dòng)與沖擊;2004年03期

相關(guān)碩士學(xué)位論文 前1條

1 陳英華;動(dòng)載荷時(shí)域Wilson-θ識(shí)別方法和PATRAN二次開發(fā)[D];南京航空航天大學(xué);2010年

，

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