重型数控铣镗床镗轴进给机构的可靠性研究

发布时间：2018-07-17 08:49

【摘要】：重型数控机床是大型基础零部件的关键加工设备,其核心传动系统从根本上制约着机床的功能输出和加工质量,其核心传动系统的可靠性问题直接影响着整机技术性能的体现,并已成为其进入世界领先水平的难题之一。重型数控铣镗床作为重型数控机床的主要成员之一,其核心传动子系统镗轴进给机构的可靠性分析实际工程意义显著。重型数控机床在现场服役期间,机械零部件的性能往往会逐渐降低直至失效,进而影响整机的性能,于是零部件与系统均会呈现出多态性。而传统的故障树分析是建立在二态假设的基础上的,无法对重型数控机床整机及零部件的多态性进行有效地描述和分析。因此,本文运用多状态故障树理论技术,从以下几个方面对镗轴进给机构的可靠性分析技术展开研究。首先,对镗轴进给机构的基本功能、结构组成以及工作环境的特殊性进行深入分析。在此基础上,对系统及其零部件进行状态定义。为突破传统分析中将系统分解成若干个二态子系统或部件来进行分析的假设缺陷,本文运用由多状态可靠性框图生成多状态故障树的建模方法,采用由各子系统到系统整机逐层向上的方式,建立镗轴进给机构的多状态可靠性框图及其多状态故障树。然后,对镗轴进给机构的多状态故障树进行定量分析。本文分别运用多值决策图与贝叶斯网络方法,采用逐层向上的分析求解方法,依次求得各子系统顶事件各状态的发生概率,直至镗轴进给机构各状态的发生概率;在此基础上,对多值决策图与贝叶斯网络分析方法的优劣进行比较分析。进一步地,本文提出基于贝叶斯网络的重要度分析方法,来对镗轴进给机构及其各子系统多状态故障树底事件的概率重要度以及关键重要度进行计算;并通过对重要度计算结果进行分析,寻找系统的薄弱环节,为系统的改进提供依据。最后,考虑到多状态系统中共因失效的影响以及可靠性信息的不确定性,本文将模糊概率与共因失效引入贝叶斯网络来开展多状态故障树分析方法的研究。采用基于贝叶斯网络的共因失效建模方法,建立镗轴进给机构的共因失效-贝叶斯网络模型。为了突破该模型的求解,首先通过引入模糊集理论,运用三角模糊数来描述根节点各状态的发生概率,并提出运用解模糊与归一化方法,以此来求解拥有重复事件的故障树顶事件各状态的发生概率。并在此基础上,对镗轴进给机构的共因失效-贝叶斯网络模型进行双向推理,识别并确认共因失效对系统可靠性的影响,为系统改进提供实用信息支撑。
[Abstract]:Heavy CNC machine tool is the key processing equipment of large basic parts. Its core transmission system fundamentally restricts the function output and machining quality of machine tool. The reliability of its core transmission system directly affects the embodiment of the technical performance of the whole machine. And it has become one of the difficulties in entering the world's leading level. As one of the main members of heavy-duty NC machine tool, the reliability analysis of the boring shaft feed mechanism of the core transmission subsystem of heavy NC milling and boring machine is of great significance in practical engineering. During the field service of heavy duty CNC machine tools, the performance of mechanical parts will decrease gradually until failure, and then affect the performance of the whole machine, so parts and systems will show polymorphism. However, the traditional fault tree analysis is based on the two-state hypothesis, which can not effectively describe and analyze the polymorphism of the whole machine parts and components of heavy NC machine tools. Therefore, in this paper, the reliability analysis technology of boring shaft feeding mechanism is studied from the following aspects by using multi-state fault tree theory. Firstly, the basic function, structure composition and working environment particularity of the boring shaft feed mechanism are analyzed. On this basis, the state of the system and its components are defined. In order to break through the hypothetical defect of decomposing the system into several two-state subsystems or components in traditional analysis, this paper uses the modeling method of generating multi-state fault tree from multi-state reliability block diagram. The multi-state reliability block diagram of boring shaft feeding mechanism and its multi-state fault tree are established by adopting the way from each subsystem to the whole system. Then, the multi-state fault tree of boring shaft feed mechanism is quantitatively analyzed. In this paper, by using multi-valued decision diagram and Bayesian network method, the probability of occurrence of each state of the top event of each subsystem is obtained in turn, and the probability of occurrence of each state of the boring axis feeding mechanism is obtained in turn by using the method of analysis and solution up to the level, and based on this, the probability of occurrence of each state of the boring axis feeding mechanism is obtained. The advantages and disadvantages of multi-valued decision graph and Bayesian network analysis method are compared and analyzed. Furthermore, the importance analysis method based on Bayesian network is proposed to calculate the probability importance and critical importance of the multi-state fault tree bottom event of the boring shaft feeding mechanism and its subsystems. By analyzing the results of importance calculation, the weak links of the system are found and the basis for the improvement of the system is provided. Finally, considering the influence of co-failure and uncertainty of reliability information, fuzzy probability and common cause failure are introduced into Bayesian network to study the method of multi-state fault tree analysis. A common-factor failure modeling method based on Bayesian network is used to establish a common-factor failure-Bayesian network model of boring shaft feeding mechanism. In order to break through the solution of the model, firstly, by introducing fuzzy set theory, using triangular fuzzy number to describe the occurrence probability of each state of root node, and putting forward the method of solving fuzzy and normalizing. In this way, the probability of occurrence of each state of the fault tree top event with repeated events is solved. On this basis, the common-factor invalidation-Bayesian network model of the boring shaft feeding mechanism is inferred, and the effect of common cause failure on the reliability of the system is recognized and confirmed, which provides practical information support for the improvement of the system.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TG659

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