论文标题
关于工业数据的数据科学 - 当今棕色现场应用程序的挑战
Data science on industrial data -- Today's challenges in brown field applications
论文作者
论文摘要
对数据分析和机器学习进行了许多研究。在工业过程中，有大量数据可用，许多研究人员正在尝试使用此数据。在实用的方法中，人们发现许多陷阱限制了现代技术的应用，尤其是在棕色现场应用中。在本文中，我们希望展示最新技术以及在现场使用库存机器时的期望。本文的主要重点是数据收集，这可能比大多数人预期的要繁琐。一旦离开实验室，机器学习应用程序的数据质量也是一个挑战。在这个领域，人们必须期望缺乏数据的语义描述，而几乎没有用于培训和验证机器学习模型的基础真相。最后一个挑战是IT安全性并通过防火墙传递数据。
Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the application of modern technologies especially in brown field applications. With this paper we want to show state of the art and what to expect when working with stock machines in the field. A major focus in this paper is on data collection which can be more cumbersome than most people might expect. Also data quality for machine learning applications is a challenge once leaving the laboratory. In this area one has to expect the lack of semantic description of the data as well as very little ground truth being available for training and verification of machine learning models. A last challenge is IT security and passing data through firewalls.
