【作者】 刘心；

【导师】 印桂生；

【作者基本信息】 哈尔滨工程大学 ， 计算机应用技术， 2010， 博士

【摘要】 柔性制造系统作为适用于中、小批量和较多品种生产的高柔性、高效率的制造系统,受到了理论界和工程界的广泛关注和深入研究。采用柔性制造技术,可以加速新产品的生产过程,降低生产成本,提高产品质量,增加生产柔性,提高对市场的应变能力,获得更好的经济效益。发展与之相适应的关键技术成为一项重要的研究课题。建模、故障诊断和可靠性分析是柔性制造系统能否实际应用的成败关键,其研究和应用的成果已经成为柔性制造系统总体应用水平的一个重要标志。本文利用Petri网这一形式化和图形化分析工具,对柔性制造系统的建模、故障诊断及可靠性分析方法进行了深入的研究,主要研究工作和取得的成果归纳如下:(1)为适应柔性制造系统结构复杂的特点,提出一种柔性制造系统UML-OOPN层次化建模方法,给出UML模型向OOPN模型转化的具体规则和算法,用不变量分析法完成了对系统的死锁分析,发挥了UML和Petri网两者的优势,支持柔性制造系统从需求分析到模型实现的全部生命周期。(2)为解决面向对象Petri网模型没有时间约束,类型约束,无法对系统进行定量分析的问题,将时间知识的表示与推理引入到面向对象Petri网中,提出一种面向对象扩展着色时间Petri网,建立了柔性制造系统的面向对象扩展着色时间Petri网模型,给出时间一致性的分析方法,采用关联矩阵分析模型的可达性,实现了柔性制造系统的时间特性定量分析,验证了建模方法的有效性。(3)为解决模糊产生式规则中的阈值和置信度难以确定的问题,提出一种自学习模糊Petri网,通过将知识模型进行分层处理后,利用神经网络的学习能力训练Petri网中的权值、阈值和确信度等参数,给出基于自学习模糊Petri网对柔性制造系统的故障诊断过程,体现了该方法能并行推理,显示故障动态传播的优点。(4)为解决随机Petri网在对复杂大规模系统可靠性分析时面临的状态空间爆炸问题,提出一种分层随机Petri网模型,采用分层随机Petri网构建了柔性制造系统的可靠性分析模型,给出系统的可靠性指标体系,编程实现了HSPN-Tool可靠性仿真分析软件,并基于分层随机Petri网的仿真模型对系统可靠性进行了仿真分析。通过仿真,分析了影响系统可靠性的关键因素。更多还原

【Abstract】 Flexible manufacturing system (FMS) is a high flexibility, high efficiency manufacturing system for small, moderate and large quantities of production, which has been concerned widely in the research of theoretical and engineering field. The flexible manufacturing technology maybe speed up the production process of new products, reduce the costs of production, improve the quality of production, increase the flexibility of production, increase the resilience of the market, and then obtain better economic benefit. So the research of the key technologies in FMS becomes an important topic. Modeling, fault diagnosis and reliability analysis are the key points to determine whether FMS can fit the practical application. The level of these three research points has become an important indication to evaluate the level of the whole FMS. As a formal and graphical analysis tools, Petri net is used in this paper for modeling, fault diagnosis and reliability analysis in FMS. The main research works and results as follows:(1) For the complex structure of FMS, a hierarchical modeling approach named UML-OOPN is proposed. The specific rules and algorithms of the UML model to OOPN model is given, using non-variable analysis method to complete the deadlock analysis of the system. The advantage of both UML and Petri has been played. The UML-OOPN can support all the needs of FMS from requirements analysis to the model realization in the full life cycle.(2) As there are no time constraints, type constraints in object-oriented Petri net model, so we can’t carry out quantitative analysis for system. A new object-oriented extended colored timed Petri net is proposed in FMS to describe the time characteristics of the system, and analyze the accessibility of the model by the correlation matrix. The quantitative analysis of time characteristics is completed and the modeling method is verified.(3) In order to solve the uncertain of threshold and confidence degree in fuzzy production rules, a new self-learning fuzzy Petri net is proposed. By the processing to knowledge model hierarchically, we use neural network learning ability to adjust the weight value, threshold and confidence degree of the knowledge model at the same time. So the system has the ability of self-learning, which can learn new expertise’s knowledge to do fault diagnosis autonomously. Fault diagnosis of FMS process based on self-learning fuzzy Petri nets are given, reflects the parallel inference that this method can show the advantages of fault dynamic spread.(4) In order to solve the possible state space explosion problem when reliability analysis work is done in a complex and large-scale system with stochastic Petri net, a hierarchical stochastic Petri net is proposed. A FMS reliability analysis model has been built to get the indicators of reliability and the system reliability index system is given. The software HSPN-Tool has been developed for the reliability of simulation and the system reliability of the simulation analysis is given by hierarchical stochastic Petri net. Through simulation, the key factors affecting system reliability is analyzed.更多还原