本文選題：巖巷掘進(jìn)機(jī) + 動載荷識別�。� 參考：《太原理工大學(xué)》2015年碩士論文

【摘要】：本課題來源于國家863計(jì)劃資源環(huán)境技術(shù)領(lǐng)域重大項(xiàng)目“煤炭智能化掘采技術(shù)與裝備（一）”子課題“智能化超重型巖巷掘進(jìn)機(jī)研制”（課題編號：2012AA06A405），是針對巖巷掘進(jìn)機(jī)工作時(shí)，工況復(fù)雜、負(fù)載多變、動載荷實(shí)時(shí)識別難度大等問題而提出的。 巖巷掘進(jìn)機(jī)的動載荷識別是掘進(jìn)機(jī)自動控制的重要組成部分，對提高掘進(jìn)機(jī)智能化水平及使用壽命具有重要意義。近年來，巖巷掘進(jìn)機(jī)在我國開采領(lǐng)域得到了越來越廣泛的應(yīng)用，但智能掘進(jìn)技術(shù)仍處于起步階段。煤礦井下現(xiàn)在使用的掘進(jìn)機(jī)大多數(shù)自動調(diào)節(jié)水平較低，以司機(jī)憑經(jīng)驗(yàn)手動操作為主。手動操作掘進(jìn)機(jī)不僅勞動強(qiáng)度大，而且因難以及時(shí)準(zhǔn)確判斷截割載荷狀態(tài)，導(dǎo)致截齒損耗嚴(yán)重。能否根據(jù)負(fù)載大小自動調(diào)節(jié)截割速度就顯得尤為重要，，而可靠的動載荷識別技術(shù)又是自動調(diào)節(jié)的必備條件。因此，開發(fā)智能化超重型巖巷掘進(jìn)機(jī)動載荷識別系統(tǒng)具有非常重要的現(xiàn)實(shí)意義。 本文在分析巖巷掘進(jìn)機(jī)截割機(jī)構(gòu)動力學(xué)特性的基礎(chǔ)上，結(jié)合先進(jìn)的信號分析技術(shù)、智能識別技術(shù)及掘進(jìn)機(jī)實(shí)際運(yùn)行情況，并通過大量動載荷模擬試驗(yàn)，開發(fā)了智能化超重型巖巷掘進(jìn)機(jī)動載荷識別系統(tǒng)。本文主要研究內(nèi)容如下： 在查閱大量相關(guān)文獻(xiàn)的基礎(chǔ)上，闡述了智能化超重型巖巷掘進(jìn)機(jī)動載荷識別系統(tǒng)在國內(nèi)外研究現(xiàn)狀及發(fā)展趨勢。深入分析了不同工況下截割機(jī)構(gòu)的載荷分布，確定了能有效反映截割頭動載荷的物理參量，主要包括懸臂振動，截割電動機(jī)電流和回轉(zhuǎn)、升降液壓缸壓力。 根據(jù)巖巷掘進(jìn)機(jī)截割機(jī)構(gòu)有限的空間范圍，選擇了適用于井下惡劣環(huán)境的多種傳感器，完成了監(jiān)測信息的準(zhǔn)確測量。結(jié)合項(xiàng)目的功能要求，設(shè)計(jì)了以數(shù)據(jù)采集卡及工控機(jī)為核心的智能化超重型巖巷掘進(jìn)機(jī)動載荷總體方案。 結(jié)合動載荷信號為隨機(jī)信號，頻率成分復(fù)雜、非平穩(wěn)的特點(diǎn)，比較了傅里葉變換、小波變換及小波包變換的優(yōu)缺點(diǎn)，確定了采用小波包變換作為信號處理及特征提取的工具。詳細(xì)介紹了小波包特征能量提取步驟，并通過分析實(shí)測數(shù)據(jù)得到了振動、電流和壓力信號的特征頻段范圍。結(jié)合所選特征頻段，選擇RBF神經(jīng)網(wǎng)絡(luò)及證據(jù)組合理論作為動載荷的智能識別方法，并對RBF神經(jīng)網(wǎng)絡(luò)和證據(jù)理論算法、結(jié)構(gòu)及應(yīng)用步驟進(jìn)行簡單分析，重點(diǎn)描述了組合神經(jīng)網(wǎng)絡(luò)信息識別原理，D-S證據(jù)理論動載荷識別原理及基于神經(jīng)網(wǎng)絡(luò)和D-S證據(jù)理論融合原理。 在LabVIEW開發(fā)環(huán)境下設(shè)計(jì)了智能化超重型巖巷掘進(jìn)機(jī)動載荷識別的相關(guān)應(yīng)用軟件，主要包括LabVIEW平臺下MATLAB代碼的調(diào)用、多傳感信號同步采集、小波包特征量提取、神經(jīng)網(wǎng)絡(luò)動載荷識別、證據(jù)理論融合、數(shù)據(jù)存儲管理以及人機(jī)顯示界面等程序，并進(jìn)行了功能調(diào)試，調(diào)試結(jié)果驗(yàn)證了應(yīng)用軟件的有效性。 根據(jù)掘進(jìn)機(jī)實(shí)際運(yùn)行情況，設(shè)計(jì)了利用回轉(zhuǎn)液壓缸及升降液壓缸壓力信號先分工況再進(jìn)行動載荷識別的方案。根據(jù)縱向鉆進(jìn)，水平切割，縱向切割三大類工況，系統(tǒng)采用三類工況識別網(wǎng)絡(luò)。以此為前提，結(jié)合截割機(jī)構(gòu)的振動數(shù)據(jù)、截割電動機(jī)的電流及液壓缸壓力數(shù)據(jù)，利用數(shù)據(jù)融合原理，構(gòu)建了一級和二級RBF神經(jīng)網(wǎng)絡(luò)多傳感器信息融合動載荷識別模型，提出了將神經(jīng)網(wǎng)絡(luò)與證據(jù)理論有機(jī)結(jié)合，優(yōu)勢互補(bǔ)的基于多神經(jīng)網(wǎng)絡(luò)與證據(jù)理論相融合的掘進(jìn)機(jī)動載荷識別新方法。并用實(shí)測數(shù)據(jù)進(jìn)行訓(xùn)練、測試及分析。
[Abstract]:This topic derives from the "intelligent mining technology and equipment of coal intelligent mining and mining", a major project of the National 863 plan resources and environmental technology field (one) "intelligent super heavy rock roadway driving machine development" (subject number: 2012AA06A405). It is a problem for the working of Rock Roadheader, which is complex, variable load and difficult to identify the dynamic load in real time. And put forward.
The dynamic load identification of the Rock Roadheader is an important part of the automatic control of the roadheader. It is of great significance to improve the intelligentized level and the service life of the roadheader. In recent years, the rock tunnel boring machine has been more and more widely used in the field of mining in our country, but the intelligent tunneling technology is still in its infancy. The most automatic adjustment level of the machine is low, and the driver is based on the experience manual operation. The manual operation boring machine not only has great labor intensity, but also makes the cutting load serious because it is difficult to judge the cutting load in time and accurately. It is particularly important to adjust the cutting speed automatically according to the load size, and the reliable dynamic load identification technique is very important. As a necessary condition for automatic regulation, it is of great practical significance to develop intelligent load identification system for ultra heavy rock roadway driving.
Based on the analysis of the dynamic characteristics of the cutting mechanism of the Rock Roadheader, this paper combines the advanced signal analysis technology, the intelligent identification technology and the actual operation of the roadheader, and develops a intelligent load identification system for the driving maneuver in the ultra heavy-duty rock roadway through a large number of dynamic load simulation tests. The main contents of this paper are as follows:
On the basis of a large number of relevant documents, the current research status and development trend of the intelligent super heavy rock roadway driving load identification system at home and abroad are expounded. The load distribution of the cutting mechanism under different working conditions is analyzed, and the physical parameters which can effectively reflect the dynamic load of the cutting head are determined, mainly including the cantilever vibration and the cutting motor. Current and rotary, lift hydraulic cylinder pressure.
According to the limited space range of the cutting mechanism of the Rock Roadheader, a variety of sensors suitable for the downhole bad environment have been selected, and the accurate measurement of the monitoring information has been completed. The overall plan of the intelligent and super heavy rock roadway driving load is designed with the core of the data acquisition card and the industrial control machine.
Combining the dynamic load signal as random signal, complex frequency component and non stationary feature, the advantages and disadvantages of Fourier transform, wavelet transform and wavelet packet transform are compared, and the wavelet packet transform is used as a tool for signal processing and feature extraction. The step of wavelet packet characteristic energy extraction is introduced in detail, and the measured data are obtained by analyzing the measured data. According to the frequency range of vibration, current and pressure signal, combining the selected frequency bands, RBF neural network and evidence combination theory are selected as the intelligent recognition method of dynamic load, and the RBF neural network and evidence theory algorithm, structure and application steps are simply analyzed, and the information recognition principle of the combined neural network is described, and the D-S certificate is described. According to the theory of dynamic load identification and the fusion principle based on neural network and D-S evidence theory.
In the LabVIEW development environment, the application software of intelligent super heavy rock roadway driving load identification is designed. It mainly includes the call of MATLAB code under the LabVIEW platform, synchronous acquisition of multi sensing signal, feature extraction of wavelet packet, neural network dynamic load identification, evidence theory fusion, data storage management and human-computer display interface, etc. The debug results verify the validity of the application software.
According to the actual running situation of the roadheader, a scheme is designed to identify the dynamic load by using the pressure signal of the rotary hydraulic cylinder and the lifting hydraulic cylinder first, and then to identify the dynamic load. According to the longitudinal drilling, the horizontal cutting and the longitudinal cutting, the system adopts the three types of working conditions to identify the network. This is the premise, combining the vibration data of the cutting mechanism to cut the electric power. With the current and pressure data of the hydraulic cylinder, using the principle of data fusion, a dynamic load identification model for multi-sensor information fusion of the first and two RBF neural networks is constructed, and a new method is proposed, which combines the neural network and the evidence theory organically, and the advantages are complementary to the multi neural network and the evidence theory. Training, testing and analysis are carried out with measured data.

【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】：2015
【分類號】：TD421.5

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