本文選題：礦井提升機(jī)　切入點：載荷辨識　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：礦井提升機(jī)作為礦山機(jī)械的重要組成部分,在礦山生產(chǎn)運(yùn)行中起著重要的"咽喉"作用。由于礦山生產(chǎn)環(huán)境的復(fù)雜性,礦井提升機(jī)往往也在復(fù)雜多變的載荷作用下工作,這些既有負(fù)載工作時的正常載荷,也包含故障狀態(tài)下的危險載荷,這與機(jī)組的安全運(yùn)行有著密切的聯(lián)系,但是受到工作環(huán)境限制,這些載荷往往難以通過直接的方法測量,因此提出了載荷辨識方法,即通過載荷下相對較易測得的動態(tài)響應(yīng)辨識載荷的方法。為了提出有效的載荷辨識方法,需要準(zhǔn)確了解礦井提升機(jī)的動態(tài)響應(yīng),在載荷作用下,首先影響礦井提升機(jī)的運(yùn)行特性,然后會影響運(yùn)行電流,以及固定支座的振動情況,考慮到電流與振動信號的復(fù)雜性與處理信號后數(shù)據(jù)誤差的傳遞性,提出從運(yùn)行特性辨識礦井提升機(jī)載荷的方法。以傳遞矩陣法假設(shè)為依據(jù)合理簡化礦井提升機(jī)物理模型,以能量守恒、功率守恒的原則對轉(zhuǎn)動慣量、剛度、轉(zhuǎn)矩做等效折算,以磁場守恒、功率守恒的原則對異步電機(jī)電磁模型進(jìn)行解耦,進(jìn)而建立MATLAB/Simulink機(jī)電耦合模型與矢量控制仿真模型,從而仿真得到礦井提升機(jī)正常載荷與故障載荷(穩(wěn)態(tài)、沖擊、瞬態(tài)、線性、正弦載荷)作用下的運(yùn)行特性曲線。發(fā)現(xiàn)速度變化特性與載荷特性有著一致的變化關(guān)系;數(shù)值上,載荷越大,速度降低也越大;而且在正弦載荷作用下,速度變化頻率與載荷變化頻率一致,頻率越大,相同幅值的載荷作用下,速度變化的幅值越小。利用這些特性,可以繞開系統(tǒng)自身特征參數(shù),以運(yùn)行特性為自變量,載荷特性為因變量,從而實現(xiàn)礦井提升機(jī)的載荷辨識。為了能夠精確辨識載荷,對比傳統(tǒng)回歸預(yù)測方法與新型回歸預(yù)測方法,引入SVM辨識載荷的方法。SVM理論完善,具有的良好的泛化能力;需要調(diào)節(jié)的參數(shù)較少,魯棒性強(qiáng);只需要簡單的優(yōu)化技術(shù),計算簡單;在解決實際問題中總是屬于最好的方法之一。因此本文采用SVM模型并與具有代表性的典型的全局尋優(yōu)算法Grid、啟發(fā)性直接算法GA與基于群體的啟發(fā)性算法PSO結(jié)合進(jìn)行載荷辨識,辨識結(jié)果表明,Grid與PSO優(yōu)化SVM往往能得到較好的辨識效果,而Grid優(yōu)化SVM的辨識效果一般優(yōu)于PSO優(yōu)化SVM,這與本文數(shù)據(jù)分布有關(guān),而且辨識結(jié)果最大的絕對誤差都發(fā)生在最小載荷的預(yù)測上,呈現(xiàn)出最大絕對誤差到最小的收斂趨勢。在數(shù)據(jù)處理方面,無論是無反饋還是反饋型SVM載荷辨識時,對自變量與因變量較好的處理方式均為自變量不歸一化-因變量不歸一化和自變量歸一化-因變量不歸一化;但是自變量歸一化與不歸一化對最終的辨識效果影響不是很大,可從二者中測試選擇最優(yōu)的,是否PCA降維處理也是如此;而且在反饋控制型SVM載荷辨識時,發(fā)現(xiàn)因變量的大小對最終的辨識效果影響比較大,通過適當(dāng)?shù)谋壤兓?可以極大的降低最大的絕對誤差,而且對整體的辨識效果影響不大,甚至可以優(yōu)化整體辨識效果。最后,研制試驗臺,采集加載數(shù)據(jù),通過試驗驗證的方法,證明了 SVM載荷辨識方法在實際應(yīng)用中的有效性,為礦井提升機(jī)的載荷辨識與故障診斷提供了理論依據(jù)。
[Abstract]:Mine hoist is an important part of mining machinery, in mine production operation plays an important role in the "throat". Due to the complexity of mine production environment, often work in machine loading under complex mine hoist, the existing load when the normal load, also contains dangerous load fault state next, has a close connection with the safe operation of the unit, but by working environment constraints, these are often difficult to load by direct measurement method, so the proposed load identification method, namely through the dynamic load is relatively easy to measure the response identification method of load. In order to put forward the effective method of load identification, need an accurate understanding of the dynamic response of mine hoisting machine, under load, the influence of operating characteristics of mine hoist, and then affect the operation of the current, and the vibration of the fixed bearing, test Considering the complexity of signal processing and transfer current and the vibration signal data after the error of the proposed method from the operation of mine hoist load characteristics identification. With the transfer matrix method based on simplified assumption of mine hoist physical model based on the energy conservation, power conservation principle of inertia, stiffness, torque and equivalent convert to the magnetic field of asynchronous motor electromagnetic model of decoupling power conservation principle, and then establish the model of MATLAB/Simulink and vector control simulation model of electromechanical coupling, and simulation machine normal load and fault of mine hoist load (steady-state, shock, transient, linear, sinusoidal load) operating characteristic curve under speed variation. And load changes have consistent relationships; numerical, the larger the load is, the greater the speed is reduced; and in sinusoidal load, velocity and load frequency Changes in the same frequency, the higher the frequency, the amplitude of the same load, the amplitude of velocity change is small. Based on these properties, its characteristics can bypass the system parameters, operating characteristics as independent variables and load characteristics as the dependent variable, so as to realize the mine hoist load identification. In order to be able to accurately identify the load forecast method, comparison the traditional regression forecasting method and regression model, improve the introduction of SVM based load identification method of.SVM theory, has the good generalization ability; the need to adjust the parameters of the less robust; only need optimization technology, a simple calculation is simple; always belongs to one of the best methods in solving practical problems. This paper uses the SVM model and the typical global optimization algorithm for load identification Grid, heuristic algorithm GA and PSO combined with direct heuristic algorithm based on population. The identification results show that Grid and PSO Optimization of SVM often can get better identification effect, and Grid identification and optimization effect of SVM is better than PSO SVM the general optimization, and the data distribution, and the identification results of the largest absolute error occurred in the prediction of minimum load, showing a trend of convergence to the maximum absolute error minimum. In the aspect of data processing, either no feedback or feedback type SVM load identification, the independent and dependent variables are way better for variables not normalized variables and independent variables for the normalized normalized variables are not normalized; but the impact of independent variables normalized and non normalized to the identification of the final result is not great, from the two test to choose the best. Whether the PCA dimension is so; but in the feedback type SVM load identification control, found that due to the impact of variable size on the identification of the final result is relatively large, through appropriate The proportion of change, can greatly reduce the maximum absolute error, but has little effect on the identification of the overall effect, even can optimize the overall identification effect. Finally, development of test bench, loading data acquisition method, through experiments, proves the validity of SVM load identification method in practical application, for the machine and load identification fault diagnosis provides a theoretical basis for the mine hoist.

【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD534

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 宋曉洋;劉立勛;;基于SVM回歸的連續(xù)血壓測量方法[J];吉林大學(xué)學(xué)報(信息科學(xué)版);2016年03期

2 傅軍棟;鄒歡;康水華;;PSO-SVM算法在智能建筑環(huán)境監(jiān)控系統(tǒng)中的應(yīng)用[J];華東交通大學(xué)學(xué)報;2016年01期

3 張公讓;萬飛;;基于網(wǎng)格搜索的SVM在入侵檢測中的應(yīng)用[J];計算機(jī)技術(shù)與發(fā)展;2016年01期

4 楊智春;賈有;;動載荷識別方法的研究進(jìn)展[J];力學(xué)與實踐;2015年01期

5 劉希亮;趙學(xué)勝;陸鋒;孫文彬;;基于GA-SVM的露天礦拋擲爆破拋擲率預(yù)測[J];煤炭學(xué)報;2012年12期

6 石瑞敏;楊兆建;;基于改進(jìn)EMD的多繩摩擦提升機(jī)載荷信息特征提取[J];煤炭學(xué)報;2014年04期

7 滕明鑫;;回歸神經(jīng)網(wǎng)絡(luò)預(yù)測模型歸一化方法分析[J];電腦知識與技術(shù);2014年07期

8 梅飛;梅軍;鄭建勇;張思宇;朱克東;;粒子群優(yōu)化的KFCM及SVM診斷模型在斷路器故障診斷中的應(yīng)用[J];中國電機(jī)工程學(xué)報;2013年36期

9 茆美琴;龔文劍;張榴晨;曹雨;徐海波;;基于EEMD-SVM方法的光伏電站短期出力預(yù)測[J];中國電機(jī)工程學(xué)報;2013年34期

10 曹善成;宋筆鋒;殷之平;黃其青;;基于支持向量機(jī)回歸的飛行載荷參數(shù)識別研究[J];西北工業(yè)大學(xué)學(xué)報;2013年04期

相關(guān)博士學(xué)位論文 前2條

1 馬馳;提升機(jī)危險載荷分析與預(yù)警系統(tǒng)研究[D];中國礦業(yè)大學(xué);2012年

2 孫德山;支持向量機(jī)分類與回歸方法研究[D];中南大學(xué);2004年

相關(guān)碩士學(xué)位論文 前4條

1 冀靈子;基于電機(jī)電流的轉(zhuǎn)子系統(tǒng)載荷識別方法研究[D];太原理工大學(xué);2016年

2 加力康;基于多源融合技術(shù)的轉(zhuǎn)子系統(tǒng)載荷識別研究[D];太原理工大學(xué);2016年

3 鄭敏;基于振動信息的轉(zhuǎn)子系統(tǒng)載荷識別方法研究[D];太原理工大學(xué);2016年

4 李菁;摩擦式礦井提升機(jī)動力學(xué)仿真及實驗研究[D];河南科技大學(xué);2014年

，

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