从前期开发到大规模制造的可扩展数据分析

H. Heimes, A. Kampker, Ulrich Bührer, Anita Steinberger, Joscha Eirich, Stefan Krotil
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引用次数: 1

摘要

数据分析提供了从大量数据中提取见解的工具集。为了保持竞争力,制造领域的公司利用数据分析来提高效率,提高生产和产品的质量。当前应用数据分析和数据挖掘技术的方法侧重于从现有系统和历史数据中寻找数据之间的相关性。因此,数据分析通常用于解决现有制造系统中的现有问题。由于目前的棕地生产线往往不能提供足够的数据,因此必须改造新的硬件以获取所需的数据。因此,浪费了解决问题的宝贵时间。本文提出了一种在早期预开发阶段主动实施数据分析的方法,以便允许该方法在大规模制造系统中的可扩展性。该方法在电动汽车高压电池制造的背景下进行了实施和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Data Analytics from Predevelopment to Large Scale Manufacturing
Data analytics provides a toolset to extract insights from large amounts of data. In order to stay competitive, companies of the manufacturing domain utilize data analytics to be more efficient and to increase quality of the production and product. Current methodologies for the application of data analytics and data mining techniques focus on finding correlations within data from existing systems and historic data. Therefore, data analytics is typically applied to solve existing problems within existing manufacturing systems. Since present brownfield production lines often provide insufficient data, new hardware has to be retrofitted to acquire the required data. Hence, valuable time for problem solving is lost. This paper presents an approach to proactively implement data analytics during early predevelopment phases in order to allow scalability of the approach to large scale manufacturing systems. The approach is implemented and evaluated within the context of high voltage battery manufacturing for electric vehicles.
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