Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland.
Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-371 Wrocław, Poland.
Sensors (Basel). 2020 Nov 13;20(22):6484. doi: 10.3390/s20226484.
The new generation Manufacturing Executions System (MES) is considered as one of the most important solutions supporting the idea of Industry 4.0. This is confirmed by research conducted among companies interested in the implementation of the Industry 4.0 concept, as well as the publications of researchers who study this issue. However, if MES software is a link that connects the world of machines and business systems, it must take into account the specifics of the supported production systems. This is especially true in case of production systems with a high level of automation, which are characterised by flexibility and agility at the operational level. Therefore, personalization of the MES software is proposed for this class of production systems. The aim of the article is to present the MES system personalization method for a selected production system. The proposed approach uses the rules of Bayesian inference and the area of customisation is the technological structure of production, taking into account the required flexibility of the processes. As part of the developed approach, the variability index was proposed as a parameter evaluating the effectiveness of the production system. Then, the results of evaluation of the current system effectiveness by use of this index are presented. The authors also present the assumptions for the developed MES personalization algorithm. The algorithm uses the rules of Bayesian inference, which enable multiple adjustments of the model to the existing environmental conditions without the need to formulate a new description of reality. The application of the presented solution in a real facility allowed for determining production areas which are the determinants of system instability. The implementation of the developed algorithm enabled control of the generated variability in real time. The proposed approach to personalization of MES software for a selected class of production systems is the main novelty of the presented research and contributes to the development of the described area of research.
新一代制造执行系统 (MES) 被认为是支持工业 4.0 理念的最重要解决方案之一。这一点得到了对工业 4.0 概念实施感兴趣的公司的研究以及研究这一问题的研究人员的出版物的证实。然而，如果 MES 软件是连接机器世界和业务系统的纽带，那么它必须考虑所支持的生产系统的具体情况。在具有高度自动化的生产系统的情况下尤其如此，这些系统在操作层面具有灵活性和敏捷性。因此，建议为这类生产系统对 MES 软件进行个性化设置。本文的目的是介绍为选定的生产系统定制 MES 系统的方法。所提出的方法使用贝叶斯推理规则，定制的领域是生产的技术结构，同时考虑到过程所需的灵活性。作为所开发方法的一部分，提出了可变性指数作为评估生产系统有效性的参数。然后，展示了使用该指数评估当前系统有效性的结果。作者还提出了为开发的 MES 个性化算法所做的假设。该算法使用贝叶斯推理规则，使模型能够根据现有环境条件进行多次调整，而无需对现实进行新的描述。在所提出的解决方案在实际设施中的应用中，确定了生产领域，这些领域是系统不稳定的决定因素。开发的算法的实现能够实时控制产生的可变性。为选定类别的生产系统定制 MES 软件的方法是本研究的主要创新点，为所描述的研究领域的发展做出了贡献。
