数控系统硬件可靠性增长技术及其措施研究
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摘要
数控系统是数控机床的核心。提高数控系统的可靠性是提高数控机床可靠性水平的重要途径。数控技术是先进制造技术的核心,大力发展数控技术,它已经成为加快经济发展,增强综合国力的重要途径。因此,提高可靠性,分析数控系统的可靠性现状,开发和应用高效的和实用的可靠性增长技术,已成为数控系统发展的关键。本文仅限于研究数控系统的最重要的部分之一硬件部分的可靠性。随着数控系统的可靠性研究的深入发展,可靠性的数据收集工作已经变得越来越重要。对30台数控加工中心机床的性能进行跟踪,对获得的数据进行分析。以确保故障数据信息的质量和数量,本文制定了一套严格的故障数据收集程序和方法,并编制了详细的规范的表格,建立了数控系统硬件的故障部位、故障原因、故障模式、故障代码的数据库。通过失效模式和效果分析(FMEA),故障模式、影响及危害性分析(FMECA),可靠性,可用性,维修分析(RAM)和故障树分析(FTA)分析,发现数控系统硬件的薄弱环节,建立可靠性增长技术和措施,以提高数控系统硬件的可靠性水平。首先,对数控系统硬件的运行状态的研究,对故障数据进行分析,建立故障定位,故障模式及其原因的评价准则。通过对故障数据分析,发现数控系统硬件频繁发生故障的部件,如电气系统、PLC单元和前处理模块,故障模式,如元器件损坏、线或电缆连接不好的,故障原因,如元件损坏。对数控系统的硬件子系统进行分析,发现了薄弱环节,并提出了可靠性设计的改进措施。基于故障数据的详细分析,建立数控系统硬件的故障树。传统的故障树分析,危害度的确定,模糊和不精确的事件包括人为的错误等往往不能有效地处理。在缺少定量数据和基本事件的概率含有模糊数时,模糊方法可能是决定故障概率值的唯一的方法。此外,模糊的方法是用来处理主观判断的有效工具。为了有效地对模糊事件或不明原因(包括人为因素)的故障进行评价,本文将专家意见与模糊集理论相结合的方式来处理故障树分析中的模糊问题。经过9个步骤的分析,提出46个最重要的故障事件根据其在顶事件的分布,这些故障事件代表了数控系统硬件需要改善的潜在故障。CPU, PLC单元,检测单元对数控系统硬件故障有很大的影响,从而改善这些事件的故障发生率以提高数控系统硬件的安全。为了确保数控系统的可靠性,可用性和可维护性以及系统整体的效率和运行能力,必须采取新的和务实RAM的分析方法。通过对数控系统硬件进行RAM统计分析,得到了相应的概率分布模型和统计特征参数,获得的分析结果可以指导数控系统硬件的操作和维修管理、确定可靠性的特点、故障的性质、故障部位、故障模式的频率,确定组件的可维护性要求等问题,以及提高数控系统硬件的可靠性。本文,基于故障数据,采用定性可靠性的方法FMEA和FMECA对数控系统的硬件的可靠性进行评估。评价指标如数控系统硬件的故障率、故障间隔时间(MTBF)、平均修复时间(MTTR)和可用性分析通过计算得出结果。在取得FMEA分析的结果之后,本文进一步确定预防／检测行为,以尽量减少故障潜在所引起的风险。数控系统的硬件优化的风险评估和减少风险优先数(RPN)被分析、.计算并得出结果。提出了可靠性评估和系统硬件可靠性改进的方法。
Computerized Numerical Control (CNC) system is the brain of CNC machine tools. Improving the reliability of CNC system is very important to improve the reliability level of CNC machine tools. Endeavoring to develop numerical control technology, which is the core of advanced manufacturing technology, has become an important way to accelerate economic development and enhance comprehensive national strength. Thus, increasing reliability, analyzing the reliability status of CNC system, as well as developing and applying advanced technologies for improving reliability have become key for the development of CNC system. The aim of this dissertation is to study the reliability of CNC system hardware, which is one of the most important parts of a CNC system. Improving CNC system hardware reliability is an important way to improve the reliability of the full CNC system. In this dissertation;the analysis data is obtained by tracking the performance of30CNC Machining Center units. Based on the tracked data, strict fault data collection procedures and methods, the preparation of a detailed specification of the failure positions, failure causes, failure modes, and failure codes, CNC system hardware database forms are proposed in order to ensure the quality and quantity of fault data information. RAM (Reliability, Availability and Maintainability), FMEA (Failure Mode and Effects Analysis), FMECA (Failure Mode and Effects Critical Analysis) and FTA (Fault Tree Analysis) are used to analyze the running CNC system hardware. Fault data, the criteria of the fault locations, fault mode, and cause are obtained from these analyses. By analyzing fault data, the frequent fault of CNC system hardware parts (electrical systems, PLC unit and pre-processing module), the fault modes (damaged components, line or bad cable connection), and frequent fault causes are thereby found. Based on the critical analysis of CNC system hardware subsystems, the weaknesses of CNC system hardware are found and methods for improving design reliability are proposed. The FTA framework of CNC system hardware is established based on the careful analysis of fault data. In traditional fault tree analysis (FTA), which is an established technique in hazard identification, ambiguous and imprecise events, such as human errors, can not be handled efficiently. Fuzzy method can generate failure probability values with only a little available quantitative information. The probabilities of basic events are treated as fuzzy numbers. Therefore fuzzy methods are effective tools to deal with subjective judgment. The method to handle the fuzzy problems in the FTA is proposed in this dissertation by combining expert elicitation with fuzzy set theory. In this dissertation we propose9analysis steps of the46most important fault events that affect the fault rate of top events to improve CNC system hardware reliability. Central Processing Unit (CPU), Programmable Logic Controller (PLC) Unit, and Detection Unit failures are common in CNC system hardware. Thus, improving these units' reliability will be the most significant contribution to enhance CNC system hardware. By analyzing the RAM system data, the corresponding probability distribution model, statistical characteristic parameters are obtained. The result of this analysis which determines the reliability characteristics, the nature of failures, the frequency of failures, the failure positions, the failure modes, and the maintainability requirements of CNC system hardware components, can be used as the reference for CNC system hardware operation, maintaining and repairing activities, as well as improving the reliability of CNC system hardware. Based on the failure data, the most well-known qualitative reliability methods, FMEA and FMECA, are used to analyze the reliability of CNC system hardware. Index evaluation of CNC system hardware such as failure rates, MTBF (Mean Time Between Failures), MTTR (Mean Time To Repairs), and are calculated to propose reliability evaluation methods for domestic CNC system hardware. After obtaining the FMEA result, further preventive/detection actions are defined in order to minimize the risk of the potential failure causes. The optimized risk assessment of CNC system hardware and the reduction in RPN (Risk Priority Number) are shown in this dissertation. Some problems of reliability evaluation are pointed out and some suggestions for system hardware reliability improvement are given.
Computerized Numerical Control (CNC) system is the brain of CNC machine tools. Improving the reliability of CNC system is very important to improve the reliability level of CNC machine tools. Endeavoring to develop numerical control technology, which is the core of advanced manufacturing technology, has become an important way to accelerate economic development and enhance comprehensive national strength. Thus, increasing reliability, analyzing the reliability status of CNC system, as well as developing and applying advanced technologies for improving reliability have become key for the development of CNC system. The aim of this dissertation is to study the reliability of CNC system hardware, which is one of the most important parts of a CNC system. Improving CNC system hardware reliability is an important way to improve the reliability of the full CNC system. In this dissertation;the analysis data is obtained by tracking the performance of30CNC Machining Center units. Based on the tracked data, strict fault data collection procedures and methods, the preparation of a detailed specification of the failure positions, failure causes, failure modes, and failure codes, CNC system hardware database forms are proposed in order to ensure the quality and quantity of fault data information. RAM (Reliability, Availability and Maintainability), FMEA (Failure Mode and Effects Analysis), FMECA (Failure Mode and Effects Critical Analysis) and FTA (Fault Tree Analysis) are used to analyze the running CNC system hardware. Fault data, the criteria of the fault locations, fault mode, and cause are obtained from these analyses. By analyzing fault data, the frequent fault of CNC system hardware parts (electrical systems, PLC unit and pre-processing module), the fault modes (damaged components, line or bad cable connection), and frequent fault causes are thereby found. Based on the critical analysis of CNC system hardware subsystems, the weaknesses of CNC system hardware are found and methods for improving design reliability are proposed. The FTA framework of CNC system hardware is established based on the careful analysis of fault data. In traditional fault tree analysis (FTA), which is an established technique in hazard identification, ambiguous and imprecise events, such as human errors, can not be handled efficiently. Fuzzy method can generate failure probability values with only a little available quantitative information. The probabilities of basic events are treated as fuzzy numbers. Therefore fuzzy methods are effective tools to deal with subjective judgment. The method to handle the fuzzy problems in the FTA is proposed in this dissertation by combining expert elicitation with fuzzy set theory. In this dissertation we propose9analysis steps of the46most important fault events that affect the fault rate of top events to improve CNC system hardware reliability. Central Processing Unit (CPU), Programmable Logic Controller (PLC) Unit, and Detection Unit failures are common in CNC system hardware. Thus, improving these units' reliability will be the most significant contribution to enhance CNC system hardware. By analyzing the RAM system data, the corresponding probability distribution model, statistical characteristic parameters are obtained. The result of this analysis which determines the reliability characteristics, the nature of failures, the frequency of failures, the failure positions, the failure modes, and the maintainability requirements of CNC system hardware components, can be used as the reference for CNC system hardware operation, maintaining and repairing activities, as well as improving the reliability of CNC system hardware. Based on the failure data, the most well-known qualitative reliability methods, FMEA and FMECA, are used to analyze the reliability of CNC system hardware. Index evaluation of CNC system hardware such as failure rates, MTBF (Mean Time Between Failures), MTTR (Mean Time To Repairs), and are calculated to propose reliability evaluation methods for domestic CNC system hardware. After obtaining the FMEA result, further preventive/detection actions are defined in order to minimize the risk of the potential failure causes. The optimized risk assessment of CNC system hardware and the reduction in RPN (Risk Priority Number) are shown in this dissertation. Some problems of reliability evaluation are pointed out and some suggestions for system hardware reliability improvement are given.
引文
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