Operational and online planning of maintenance and production

维护和生产的运营和在线规划

• 批准号: 257820368
• 负责人: Professor Dr.-Ing. Berend Denkena
• 金额: --
• 依托单位: WEBfactory GmbH
• 依托单位国家: 德国
• 项目类别: Research Grants (Transfer Project)
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/257820368?language=en
• 关键词: Operational; online; planning; maintenance; production

The central conflict of production and maintenance is the fact that maintenance measures have to ensure a high availability of machines. However, for their implementation a machine downtime is often required at short notice. Especially in complex, multi-stage and high utilized production systems, the conflicting behavior of production and maintenance planning is particularly pronounced, since the machine downtime can quickly cause production losses and failure consequences for the entire production system. Existing scientific approaches do not solve these issues, since the complexity of real production systems is mapped inadequate and under very restrictive assumptions (e. g. single machine approaches, deterministic and static decision environment).In the framework of the DFG-funded scientific project MK-ProInst, a dynamic planning approach to coordinate the production and maintenance planning was developed and implemented by means of discrete event simulation (DES) at the Institute of Production Engineering and Machine Tools (IFW). This approach allows a robust prediction and quantification of the resulting failure consequences that arise when implementing a maintenance measure during production. Moreover, planning alternatives can be derived and evaluated (e. g. alternative maintenance starting time). A validation of the approach showed that the production and maintenance costs can be reduced by up to 9 %. Furthermore, the planning quality is significantly increased since the complexity, dynamics and stochastics of real production systems are considered using the simulation technology in decision making. The basis for industrial exploitation of the methodology is to reduce the time needed for model creation and adaption and to show a low model validity at an early stage.The goal of the knowledge transfer project is the exploitation of the dynamic planning approach to achieve the essential foundation for an efficient and online application for a variety of companies. For this, the existing planning approach will be expanded with regard to methods for learning and self-parameterization of process-oriented simulation models based on data from BDE-/MDE-Systems (e. g. for stochastic data). In addition, appropriate feedback loops will be developed to the timely recognition of a low model validity. These aspects are the basis for time-efficient model creation and adaption and the operational (online) application in practice.

生产和维护的核心冲突是维护措施必须确保机器的高可用性。然而，为了实现它们，通常需要在短时间内通知机器停机。特别是在复杂、多阶段和高利用率的生产系统中，生产和维护计划的冲突行为尤其明显，因为机器停机会很快导致整个生产系统的生产损失和故障后果。现有的科学方法无法解决这些问题，因为实际生产系统的复杂性映射不充分并且受到非常严格的假设（例如单机方法、确定性和静态决策环境）。在 DFG 资助的科学项目 MK-ProInst 的框架内，通过生产工程和机械研究所的离散事件模拟 (DES) 开发和实施了一种协调生产和维护计划的动态规划方法。 工具（IFW）。这种方法可以对生产过程中实施维护措施时出现的故障后果进行可靠的预测和量化。此外，可以导出和评估计划替代方案（例如替代维护开始时间）。该方法的验证表明，生产和维护成本最多可降低 9%。此外，由于在决策中使用仿真技术考虑了实际生产系统的复杂性、动态性和随机性，因此规划质量显着提高。该方法的工业开发的基础是减少模型创建和适应所需的时间，并在早期阶段表现出较低的模型有效性。知识转移项目的目标是利用动态规划方法，为各种公司实现高效的在线应用奠定必要的基础。为此，现有的规划方法将在基于 BDE/MDE 系统数据（例如随机数据）的面向过程的仿真模型的学习和自参数化方法方面得到扩展。此外，将开发适当的反馈循环，以便及时识别低模型有效性。这些方面是高效模型创建和调整以及实践中操作（在线）应用的基础。