Drill bit wear monitoring and failure prediction for mining automation

This article introduces a novel approach for tricone bit wear condition monitoring and failure prediction for surface mining applications.A successful bit health monitoring system is essential to achieve fully autonomous blasthole drilling.In this research in-situ vibration signals were analyzed in timefrequency domain and signals trend during tricone bit life span were investigated and introduced to support the development of artificial intelligence(AI)models.In addition to the signal statistical features,wavelet packet energy distribution proved to be a powerful indicator for bit wear assessment.Backpropagation artificial neural network(ANN)models were designed,trained and evaluated for bit state classification.Finally,an ANN architecture and feature vector were introduced to classify the bit condition and predict the bit failure.

Sensing for advancing mining automation capability:A review of underground automation technology development

This paper highlights the role of automation technologies for improving the safety, productivity, and environmental sustainability of underground coal mining processes. This is accomplished by reviewing the impact that the introduction of automation technology has made through the longwall shearer automation research program of Longwall Automation Steering Committee(LASC). This result has been achieved through close integration of sensing, processing, and control technologies into the longwall mining process. Key to the success of the automation solution has been the development of new sensing methods to accurately measure the location of longwall equipment and the spatial configuration of coal seam geology. The relevance of system interoperability and open communications standards for facilitating effective automation is also discussed. Importantly, the insights gained through the longwall automation development process are now leading to new technology transfer activity to benefit other underground mining processes.

Automation in U.S. longwall coal mining: A state-of-the-art review

This paper reviews the development of U.S. longwall mining from an unknown to became the world standard in the past five decades with emphasis on automation. Large scale longwall face equipment were imported from Germany and United Kingdom to increase production in the 1970 s and great effort was made to improve them to suit U.S. conditions, rather than domestic market. Automation began with the development of electrohydraulic shields in 1984 and continue to present. Introduction of first generation semi-automated longwall system occurred in 1995 and step-to-step improvement continues to present following the development of sensor technology and internet of things(IOT). Since then, emphasis on new development has been concentrated on the improvement of equipment reliability, miner's health and safety as well as production, including dust control techniques, proximity sensor, anti-collision and remote control. Automation is classified into two categories: automation of individual face equipment and automation of longwall system. The automation development of longwall system is divided into three stages: shearer-initiated-shield-advance(SISA), semi-automated longwall system, and remote control shearer.

Waste-filling in fully-mechanized coal mining and its application

A fully-mechanized coal mining (FMCM) technology capable of filling up the goaf with wastes (including solid wastes) is described. Industrial tests have proved that by using this technology not only can waste be re-used but also coal resources can be exploited with a higher recovery rate without removing buildings located over the working faces. Two special devices, a hydraulic support and a scraper conveyor, run side-by-side on the same working face to simultaneously realize mining and filling. These are described in detail. The tests allow analysis of rock pressure and ground subsidence when backfilling techniques are employed. These values are compared to those from mining without using backfilling techniques, under the same geological conditions. The concept of equivalent mining height is proposed based on theoretical analysis of rock pressure and ground subsidence. The upper limits of the rock pressure and ground subsidence can be estimated in backfilling mining using this concept along with traditional engineering formulae.

Longwall automation: Delivering enabling technology to achieve safer and more productive underground mining

The ongoing need to deliver improved safety, productivity and environmental benefit in coal mining presents an open challenge as well as a powerful incentive to develop new and improved solutions. This paper assesses the critical role that enabling technologies have played in the delivery of remote and automated capability for longwall mining. A brief historical account is given to highlight key technical contributions which have influenced the direction and development of present-day longwall technology. The current state of longwall automation is discussed with particular attention drawn to the technologies that enable automated capability. Outcomes are presented from an independently conducted case study that assessed the impact that CSIRO＇s LASC longwall automation research has made to the longwall mining industry in Australia. Importantly, this study reveals how uptake of this innova- tive technology has significantly benefitted coal mine productivity, improved working conditions for personnel and enhanced environmental outcomes. These benefits have been widely adopted with CSIRO automation technology being used in 60 per cent of all Australian underground operations. International deployment of the technology is also emerging. The paper concludes with future challenges and opportunities to highfight the ongoing scope for longwall automation research and development.

Environmental impact improvements due to introducing automation into underground copper mines

A life-cycle assessment(LCA) model was developed to comparatively analyze the use of manual and automated mining equipment in underground copper mine sites.Processes and key variables that were determined to contribute to the environmental impact of operations were identified for six mine sites in a range of geographical locations around the world.Our model successfully calculated carbon dioxide(CO_(2) eq.) emissions to within 4.9% of the reported annual emissions from the site's respective companies.The implementation of automation was found to decrease global warming potential by a range of 11.4%-18.0% or 3.9-17.9 kg CO_(2) eq./t ore.The model was also used to estimate the average reductions across several impact potentials including,acidification(11.9%-17.8%),eutrophication(7.6%-13.7%),and human toxicity(16.0%-20.0%).World-wide the mining industry is moving toward introducing significantly more automation to enhance productivity and safety.This novel work demonstrates an important third dimension that can support this move,reduced environmental impact.

Analysis and control of hydraulic support stability in fully-mechanized longwall face to the dip with great mining height

The working condition of the hydraulic support in working face can be divided into three kinds of situations in the following： roof fall and col,lapse with cavity, advancing support and supporting. Took single support with four-pole in Iongwall face to the dip as research object, control method was studied to avoid support instability in three situations mentioned above. Based on these researches, the major factors of influencing on support stability and its controlling measures were put forward. According to specific conditions of working face 1215（3）, which is fully-mechanized and Iongwall face to the dip with great mining height in Zhangji Coal Mine, Huainan Mining Group, the effective measures was taken to control supports stability..

Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes

To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.

基于Active X Automation的三维可视开采沉陷预计

为弥补现有开采沉陷预测程序在可视化表达中的缺陷,采用VB和SURFER的Active X Automation技术开发了基于概率积分法的开采沉陷预测分析系统。通过VB语言操纵SURFER内核程序实现开采沉陷的各种移动变形等值线、三维表面图及剖面图制作和数据分析的自动化。以山东某煤矿多工作面、多煤层开采沉陷预计对所建立分析系统进行验证。结果表明,采用VB与SURFER结合用于开采沉陷的预测分析,能够满足工程需要,并且能极大地提高工作效率,减少程序开发的工作量,实现开采沉陷预测分析图件制作的专业化、自动化。

基于Mapobject组件和OLE Automation技术的矿山环境信息管理系统

从矿山环境信息管理系统研制的角度入手,介绍了在应用ESRI公司组件Mapobject二次开发过程中的两项关键技术:(1)后台SQLServer2000数据库与Mapobject空间数据转换与关联,极大地丰富了图元的信息量;(2)应用Surfer8提供的OLE Automation技术进行二次编程开发,实现了彩色等值线图和三维立体渲染图的绘制。通过上述两项措施,扩展了MapObject的信息源、弥补了MapObject的不足,完善了矿山环境信息管理系统的功能。

ActiveXAutoMation在绘制开拓平面图中的应用

从分析矿井开拓平面图图样入手 ,针对如何运用ActiveXAutoMation编程技术以实现自动绘图的问题 ,从计算方法和程序设计两个角度作了详尽的说明 。

Longwall automation:trends,challenges and opportunities

This paper explores the ongoing development and implementation of longwall automation technology to achieve greater levels of underground coal mining performance. The primary driver behind the research and development effort is to increase the safety, productivity and efficiency of longwall mining operations to enhance the underlying mining business. A brief review of major longwall automation challenges is given followed by a review of the insights and benefits associated with the LASC longwall shearer automation solution. Areas of technical challenge in sensing, decision support, autonomy and human interaction are then highlighted, with specific attention given to remote operating centres, proximity detection and systems-level architectures in order to motivate further automation system development.The vision for a fully integrated coal mining ecosystem is discussed with the goal of delivering a highperformance, zero-exposure and environmentally coherent mining operations.

SVR-Miner:Mining Security Validation Rules and Detecting Violations in Large Software

For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this paper,we propose a new approach,named SVR-Miner(Security Validation Rules Miner),which uses frequent sequence mining technique [1-4] to automatically infer implicit security validation rules from large software code written in C programming language.Different from the past works in this area,SVR-Miner introduces three techniques which are sensitive thread,program slicing [5-7],and equivalent statements computing to improve the accuracy of rules.Experiments with the Linux Kernel demonstrate the effectiveness of our approach.With the ten given sensitive threads,SVR-Miner automatically generated 17 security validation rules and detected 8 violations,5 of which were published by Linux Kernel Organization before we detected them.We have reported the other three to the Linux Kernel Organization recently.

Infrastructure-based localisation of automated coal mining equipment

A novel radar-based system for longwall coal mine machine localisation is described. The system, based on a radar-ranging sensor and designed to localise mining equipment with respect to the mine tunnel gate road infrastructure, is developed and trialled in an underground coal mine. The challenges of reliable sensing in the mine environment are considered, and the use of a radar sensor for localisation is justified. The difficulties of achieving reliable positioning using only the radar sensor are examined. Several probabilistic data processing techniques are explored in order to estimate two key localisation parameters from a single radar signal, namely along-track position and across-track position, with respect to the gate road structures. For the case of across-track position, a conventional Kalman filter approach is sufficient to achieve a reliable estimate. However for along-track position estimation, specific infrastructure elements on the gate road rib-wall must be identified by a tracking algorithm. Due to complexities associated with this data processing problem, a novel visual analytics approach was explored in a 3D interactive display to facilitate identification of significant features for use in a classifier algorithm. Based on the classifier output, identified elements are used as location waypoints to provide a robust and accurate mining equipment localisation estimate.

Fully Automated Density-Based Clustering Method

Cluster analysis is a crucial technique in unsupervised machine learning,pattern recognition,and data analysis.However,current clustering algorithms suffer from the need for manual determination of parameter values,low accuracy,and inconsistent performance concerning data size and structure.To address these challenges,a novel clustering algorithm called the fully automated density-based clustering method(FADBC)is proposed.The FADBC method consists of two stages:parameter selection and cluster extraction.In the first stage,a proposed method extracts optimal parameters for the dataset,including the epsilon size and a minimum number of points thresholds.These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters.The proposed method was evaluated on different benchmark datasets andmetrics,and the experimental results demonstrate its competitive performance without requiring manual inputs.The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method,k-means,spectral clustering,DBSCAN,FCDCSD,Gaussian mixtures,and density-based spatial clustering methods.It can handle any kind of data set well and perform excellently.

Study on safe thickness of overlying thin bedrock in fully-mechanized top-coal caving face with thick coal seam

To prevent support crush, the overlying strata safe thickness and its influential elements were studied by the adoption of theoretical analysis, numerical simulation and in-situ measurement. According to the production and geological condition of first face in Sima coal mine, the results indicate that the clay contains large permissible bearing ability and has better arching force. After mining destruction, stable structure is formed in bedrock to ensure face safety. The clay thickness ＆ bedrock thickness are the key influential elements to stable structure. The minimal bedrock thickness is about 40 m to ensure safe mining under loose surface soil condition. When surface soil contains mainly thick clay, it forms steady structure with the composition of thin bedrock, so that it can reduce minimal thickness of bedrock and to ensure safe mining. When clay thickness is 40 m, minimal bedrock thickness is 20 m. When clay thickness is 30 m, minimal bedrock thickness is 30 m. Bearing pressure peak ranges from 5 to 15 m in the front face under thin bedrock condition. The bearing pressure distribution range is 15 m. Main roof break distance is small, and initial weighting of main roof is not distinctive, while first periodic weighting of main roof is quite distinctive.

我国金属矿山智能化现状与问题探讨

我国金属矿山智能化在经历了前期的探索性研究后,已进入了规模化、常态化应用的新阶段。为了系统评价已形成的研究成果、保证前期积累得以顺利移植和推广,以智能生产和集成平台两类代表性应用为主要内容,总结了金属矿山智能化发展现状和应用效果。结果表明:我国金属矿山在智能开采领域取得了长足的进步,露天矿山的遥控铲装运输、地下矿山的电机车自动化、固定设施的无人值守等项目已开始常态化应用;在集成平台建设上具有鲜明的特色,技术平台、安全平台、集控平台和决策平台已在多个矿山的生产管控中发挥作用。最后,结合现状分析着重探讨了矿山在建设内容选择、关键技术确定、数字化转型路径、管理模式转变、运维与应用效果等方面出现的问题,指出了容易产生的误区,为矿山企业扩大建设成果、保证建设实效、规避建设风险提供参考。

智能技术在煤矿电气工程自动化中的应用

为进一步探究煤矿电气工程自动化领域的智能发展,结合煤矿电气工程自动化建设的实际需要,以“云平台”的设计理念,整合云计算等功能构建煤矿综合自动化集成系统平台,并整合模糊控制、聚类算法等智能算法,确保智能技术得到有效应用,以此实现对煤矿电气工程领域中相关设备的有效监管作业。

基于Digital Mine架构的开磷现代化矿山建设

开磷矿业根据矿业市场经济形势和矿山可持续发展需要,提出了建设数字开磷的发展策略.数字开磷以生产自动化、管理信息化和效益最大化为目标,主要由数字矿床勘查系统、采矿系统、安全监测预警系统、管理信息系统及其决策支持系统构成.笔者结合矿山实际从数字矿山的内涵和技术层面来反映开磷矿业现代化矿山建设进程.

基于自动化私有协议识别的挖矿流量检测

面向虚拟货币挖矿过程中私有协议流量检测识别需求,提出面向未知挖矿行为通信协议流量的自动化识别方法。改进N-gram报文格式分割算法和字典树正则表达式生成算法,实现私有协议特征签名自动化生成,对明文通信的挖矿流量进行精确匹配;基于经典加密流量分类模型,改进基于流交互特征的流量分析方法,实现轻量级的挖矿行为识别模型,对加密通信的挖矿流量进行实时检测。测试结果表明:所提方法生成的挖矿通信协议特征签名可覆盖当前3种主流明文挖矿流量,在实网验证过程中可达到0.996的识别精确率和0.985的召回率。