采煤机 毕业论文外文翻译

采煤机 毕业论文外文翻译 英文原文 Utilisation of Data Mining in Mining Industry: Improvement of the Shearer Loader Productivity in Underground Mines I. INTRODUCTION The longwall system is the heart of the coal mining process. It is considered to have a simple layout but requires stringent adherence to some basic features in the development of the panel to make it work. Similarly, in order to produce the required business production volume, all critical equipments must operate to their required level of service according to the system’s established operating mission profile. The integrated nature of production system demands that all units or sub-systems should function with acceptable level of reliability to achieve planned production. The highly productive underground longwall mining equipment is very expensive and it requires proper assessment of serviceability of each component for maintenance management. Shearer is used for getting coal in a longwall face and its breakdown is catastrophic in financial terms. Therefore, its availability is a must for the economic sustainability of the project. If the shearer is not working efficiently, this may reduce the efficiency of the entire production system [1]. In the case of the highly mechanised shearer loader, an annual coal production target of four million tons was established to achieve the annual revenue target. To achieve this level of productivity, the shearer is expected to operate at 40 weeks-operating year at 106 hours per week and allowing for only two longwall moves in a year. This tight target seems to be unrealistic considering the current increasing failure rate of the overall system. This unreliable condition only holds true in the absence of correct and auditable strategic maintenance and reliability policy, management systems and procedures in place. In this industrial case a substantial amount of time is being put together to systematically extract the appropriate data and analyse a few factors. The factors include the cause of failure, current maintenance regime, maintenance system and procedures, quality of computerised equipment historical records, data management and analysis and human reliability. This is in order to identify gap and implement effective solutions to the shearer’s current reliability shortcomings. The goal of this study is to develop a procedure to optimize maintenance plan for the Shearer Loader that would minimize costly production losses and improve reliability. The program is to consider long-term plant operation (with possible equipment life extension) by continuing with processes that have provided excellent past performance, proceeding with existing maintenance improvement programs, and recommend new cost effective maintenance task. This study aimed at achieving the following objectives to drive delivery of the end goal: 1) To identify a cost-effective maintenance strategy by reviewing, rationalising and optimising existing maintenance task. 2) Apply design methodology in order to increase the level of service from its current performance; improve life cycle cost and reliability of the Shearer over its design mission life. 3) To develop guidelines for operation and maintenance integration 4) Identify effective means of recording and utilising meaningful reliability data for performance analysis. 5) Ensure plant equipment is maintained appropriately byconsidering its importance to safety, reliability, and availability. The study also highlighted the significance of risk based assessment methodology such as FMEA/FMECA, Planned Maintenance Optimisation (PMO). The practice and principles of reliability and maintainability modelling has proven to generate increased profitability among world leader in mining industry. This method was found useful as an effective planning and operational tool for automatic and highly complex mine production system while employing almost without redundancy high cost critical equipment i.e.,the Shearer Loader. This is where Return on Investment (ROI) can be justified by longer MTBF before scheduled maintenance task could interfere [2-3]. The period of this study, where data were mined and analysed, was February – August 2010. Interestingly, a positive improvement in performance of the shearer has been observed since the early implementation of the analysis findings. II. THE FUNCTION OF THE SHEARER LOADER The shearer loader installed in the underground mining is a Double Ended Ranging Drum Shearer (DERDS) equipped with outboard ranging arms. It is designed and manufactured to extract and load coal into the Armoured Face Conveyor which is also a critical integral part of the longwall mining system. The Shearer operates within a minimum seam range of 1.8 metres and a maximum of 3.0 metres with an undercut of not less than 150mm with a 1000mm web. The Shearer is also capable of both Half Web and Bi-Directional cutting and loading the full seam section at rated capacity to achieve the production requirements up to 4.0 million tonnes from the Longwall, in a 40 week operating year, with a weekly average of 100,000 tonnes. This operating schedule allows for two Longwall moves per year. The Shearer Loader is expected to achieve the equipment availability of not less than 98% of any given month, when operated 5-days a week (106 hours per week). Figure 1 shows the major components of the shearer loader. III. RELIABILITY ISSUES AND RISK ASSESSMENT METHODOLOGY The shearer failed to meet the availability performance target within the period due to several preventable failure modes that causes business interruptions. A SWOT analysis that was conducted from the start of this study shows the following factors directly impacting the shearer performance; 1. Planning and scheduling done in ad-hoc basis 2. Equipment hierarchy limited to high level and does not capture failure modes to its component and sub-system component 3. Compliance to maintenance schedule is inconsistent 4. Poor data utilisation 5. Backlog not measured 6. Work order package incomplete (SWMS, JSA, drawings, BOM,etc.) A reliability analysis and comprehensive Failure Mode Effect and Criticality (FMECA) were carried out to identify failure rates of individual critical maintainable items and failure modes. The outcome of this process concluded with chronic reappearance of the same failed maintainable items caused by the same failure modes and failure mechanism that is causing the loss of its function. A Pareto analysis was used to identify a list of problems, using criteria of highest total revenue loss, breakdown frequency, Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR) [4]. Pareto analysis identifies trailing cable failure as a leading contributory to cause business interruption. The sub-causes of this failure include the physical damage which occur majority of time and minor recurrence cause by electrical protection device malfunctioning and electrical faults. Root causes physical damage to trailing cables was further identified using the Apollo Root Cause Analysis. Apollo Root Cause Analysis is a problem solving technique that operates in principle that “Things don’t just happen, they are made to happen.” Cause and effect relationships govern everything that happens and as such are the path to effective problem solving [5]. This cause and effect relationship identifies failure modes highlighted in in the FMECA process. Furthermore, additional business interruption is also driven by other cause factors with the most number of downtime as follows: 1.Electrical system failure 2.Control and monitoring system failure 3.Mechanical haulage failure IV. FIELD DATA COLLECTION & FEEDBACK Data collection from the field and on-line monitoring system must be accurate and realistic. It provides a starting point of generating formal failure-reporting document and ensures that the feedback is both consistent and adequate [6]. PULSE is a Computerised Maintenance Management System (CMMS) used currently in the underground mine. It is a central database and a depository of all pertinent data relating to the Plant Register. The system is capable of providing the following information: 1.Work order type and priority 2.Selecting maintenance type 3.Raising requisition from work order 4.Equipment information and Bill of Materials (BOM) 5.Equipment history 6.Suppliers information and cost reporting 7.Estimates (time and labour information) 8.Real time capture of the operating time of the asset in a 24-hours/day The system also provides links to other critical information to effectively carry out the required work such as: 1.Job plans/ Standard works 2.Safe work procedures 3.Work Permits 4.Risk assessment 5.Drawings and other technical references The software organises the equipment hierarchy starting from the Plant Register level and extending: 1.Two levels downwards, to Sub-Assembly and Component; 2.Two levels upwards, to Plant Group and Site Code. Outside the Plant Hierarchy, PULSE uses a user defined key field,"Location", which is intended to indicate the equipment’s physical location within the site’s boundaries. V. CONCLUSION The global trend in longwall coal mining production system is towards ever increasing productivity with the least operating and maintaining cost possible. This trend is reflected nowhere better than in the increasing production expectations from the existing longwall installation and the next generation of longwall installation. Subsequently, the demand for the shearer loader to increase longwall production is accompanied by the increased in the level of service and high level of automation to ensure a safe and efficient working environment for the operators combined with consistent coal face equipment management regime. However, profitability depends on having the means to accurately diagnose any performance issues before they lead to production downtime. Therefore, it is vital to have the right mix of satisfied and reliable working personnel using correct fault detection tools, optimised maintenance task-oriented and becoming knowledgeable and actively involved on FMECA and root cause analysis to deliver value to the business. The study proved that it is the right way to improve reliability of the shearer. Qualitatively, the maintenance resource utilisation becomes efficient in controlling and safely managing failure modes of the shearer. It is where items such as technology and control mechanism are applied to enable the processes to reach the maximum potential. Also, this study shows that the computerised acquisition of an accurate and reliable data and application of reliability tool such as FMECA, Weibull analysis and problem solving technique are essential in understanding and managing failure behaviour and performance of the shearer loader over its operating life span. Excellence is not a static standard. The bar is always being raised; so sustained peak performance of the shearer requires continuous improvement based on its history data. Effective utilisation of data analysis and industrial informatics can help us achieve this objective. 中文译文 I.介绍 长壁煤炭开采过程中系统的心脏。它被认为是有一个简单的布局，但需要 严格遵守的面板，使其工作在发展的一些基本特征。同样，以产生所需的业务 产量，所有关键设备必须工作到他们所需的服务级别，根据系统的建立工作任 务剖面。生产系统的综合性质，要求所有单位或子系统应该能够在可接受的水 平，可靠性，实现 计划 项目进度计划表范例计划下载计划下载计划下载课程教学计划下载 生产。高生产力的井下长壁开采设备是非常昂贵的，它 需要适当的评估每个组件的维护管理可维护性。希勒用于在工作面的煤炭，其 故障是灾难性的财务条款。因此，它的可用性是一个必须为经济的可持续发展 项目。如果采煤机不能有效地工作，这可能会降低效率的整个生产体系[1]。煤 炭年产量400万吨的目标在高度机械化采煤机的情况下，建立了实现全年收入 目标。要做到这一点的生产力水平，采煤机预计在106小时，每星期工作在40 周营运年度，并允许在一年内只有两个工作面移动。这种紧密的目标似乎是不 现实的，考虑到目前的增加了整个系统的故障率。这种靠不住的情况缺乏正确 和可审计的战略维护和可靠性的政策， 管理制度 档案管理制度下载食品安全管理制度下载三类维修管理制度下载财务管理制度免费下载安全设施管理制度下载 和程序，只有拥有真正的。在 这个工业的情况下，大量时间被放在一起，系统中提取相应的数据，并分析了 几个因素。这些因素包括失败的原因，目前的保养维修制度，维护制度和程序， 电脑设备的历史记录的质量，数据管理和分析，可靠性和人力。这是为了找出 差距和有效的解决方案，以实现采煤机的可靠性缺点。 这项研究的目标是开发一个程序来优化维修计划，采煤机，将最大限度地减少昂贵的生产损失，提高供电可靠性。该计划是要考虑长期的工厂操作(有可能会导致设备寿命延长)，通过持续的过程，提供了优异的过往表现，继续与现有的维修改进方案，并建议新的成本有效的维护任务。 本研究旨在实现以下目标驱动交付的最终目标: 1)要找出一个符合成本效益的维护策略，通过审查，合理化和优化现有的维 护任务。 2)应用设计的方法，以提高服务水平，从目前的表现，希勒在其设计任务寿 命，提高生命周期成本和可靠性。 3)制定政策，操作和维护一体化 4)确定进行性能分析的有意义的可靠性数据的记录和利用的有效手段。 5)确保工厂设备保持适当byconsidering其安全性，可靠性和可用性的重要 性。 该研究还强调风险评估为基础的方法，如FMEA / FMECA的意义，计划维 护优化(PMO)。的做法和原则已被证明的可靠性和可维护性建模生成在世界 领先的采矿业的 盈利能力增加。此方法可作为一个有效的规划和操作的 工具，自动化和高度复杂的矿井生产系统，而采用几乎没有冗余成本高，关 键设备，即采煤机。这是可以合理的投资回报率(ROI)更长的平均无故障 时间，之前预定的维护任务可能会干扰[2-3]。这项研究期间，如果数据被 开采和分析，是2月 - 2010年8月。有趣的是，积极改善采煤机的性能已 被观察到初以来实施的分析结果。 二。采煤机的功能 安装在地下开采的采煤机是一个双端测距滚筒采煤机(DERDS)配备舷外摇臂。它的设计和制造，提取和加载到刮板输送机，这也是一个重要的组成部分的长壁开采系统煤炭。希勒缝范围内最低为1.8米和3.0米，底切不小于150mm，1000mm的网页最多。希勒还能够两个半Web和双向切割和装载全煤部分在额定容量，实现了生产的要求从4.0万吨长壁，在40周的经营年度，平均每周为100,000吨。此作业时间表允许每年的两个长壁移动。有望实现采煤机运行时，设备利用率不低于98,的任何一个月，每周5天(106小时，每星期)。图1示出的采煤机的主要组成部分。 III。可靠性问 题 快递公司问题件快递公司问题件货款处理关于圆的周长面积重点题型关于解方程组的题及答案关于南海问题 和风险评估方法 采煤机未能满足可用性性能目标的期间内，由于一些可预防的故障模式，会导致业务中断。本研究从一开始就进行的SWOT分析显示下列因素直接影响采煤机性能; 1。在特设的基础规划和调度 2。仅限于较高水平，设备层次不捕获它的组件和子系统组件的故障模式 3。符合维护时间表不一致 4。数据利用差 5。积压未测 6。工单包不完整(SWMS，JSA，图纸，BOM等) 识别个别关键项目的维护和故障模式的故障率进行了可靠性分析和全面的失效模式影响及危害性(FMECA)。这个过程的结果，得出的结论与慢性再现相同的维护的项目失败所造成的同样的故障模式和失效机制，其功能造成的损失。帕累托分析，以确定问题的列表，使用标准最高的总收入损失，故障频率，平均故障间隔时间(MTBF)和平均修复时间(MTTR)[4]。 Pareto分析识别尾随电缆故障导致业务中断作为一个领先的分担。这种故障的原因包括:大多数电气保护装置故障和电气故障的时间和轻微的复发原因发生的物理伤害。根源物理损坏拖缆使用阿波罗根本原因分析进一步确定。阿波罗根本原因分析是一个解决问题的技术，经营原则，“事情不只是发生，他们的情况发生。”的原因和影响 关系治理所发生的一切，如有效的解决问题[5]的路径。这个因果关系的确定突出显示的在FMECA进程中的故障模式。此外，额外的业务中断也带动其他原因因素与数量最多的停机时间如下: 1.电动系统失败 2，控制和监测系统故障 3，机械牵引故障 四。现场数据采集反馈 从现场和在线监测系统的数据收集必须是准确的和现实的。它提供了一个生成正式的故障报告文件的起点和，确保反馈是稳定和足够的[6]。脉冲是一个电脑化维修管理系统(CMMS)目前在煤矿井下使用的。它是一个中央数据库所有相关数据有关植物寄存器和存。该系统能够提供以下信息: 1。工作订单类型和优先级 2.Selecting维护类型 3.Raising征用工作秩序 4。设备信息和材料清单(BOM) 5，设备历史 6，供应商信息和成本报告 7.Estimates(时间和劳动资料) 8.固定资产时间捕捉资产的运行时间在24-hours/day 该系统还提供了其他关键信息的链接，有效地开展所需的工作，如: 1.Job计划/标准工程 2.Safe工作程序 3.工作许可证 4.Risk评估 5.Drawings和其他技术参考 软件组织的设备层次，从植物寄存器级别和延伸: 1.两个级别向下，子装配和组件; 2.两个水平向上，植物类群和网站代码。 工厂层级之外的，脉冲使用用户定义的关键领域，“位置”，这是为了表明设备的物理位置站点的边界内。 五.结论 长壁煤炭开采生产系统的全球趋势是朝着不断提高生产力，用最少的操作和维护成本成为可能。可以看出这种趋势优于从现有的长壁安装和下一代的长壁安装在增产预期不通。随后，采煤机的需求，以增加工作面的生产是伴随着增加服务和自动化程度高的水平，以确保安全和高效的工作环境，为运营商一致的采煤工作面的设备管理制度结合。然而，盈利能力取决于拥有的手段，准确地诊断任何性能问题，才导致生产停机时间。因此，至关重要的是有满意和可靠的工作人员使用正确的故障检测工具的正确组合，优化维护任务为导向，成为知识渊博，积极参与FMECA和根源分析提供的商业价值。研究证明，这是正确的方式来提高采煤机的可靠性。定性，维护资源的利用率变得有效的控制和安全管理的采煤机的故障模式。是技术和控制机制，如项目的应用，使过程达到最大的潜力。此外，这项研究表明，电脑收购一个准确可靠的数据和应用程序的可靠性工具，如FMECA，威布尔分析和解决问题的技术是必不可少的理解和管理失败的行为和性能的采煤机在其工作寿命。卓越不是一个静态的标准。酒吧总是被提出，所以持续的峰值性能的采煤机需要历史数据的基础上不断改进。有效地利用数据分析和工业信息可以帮助我们实现这一目标。