Introduction of a time series machine learning methodology for the application in a production system
Introduction of a time series machine learning methodology for the application in a production system
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DOI：
10.1016/j.aei.2020.101197
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发表时间：
2021-01-01
影响因子：
8.8
通讯作者：
Rosenberger, Patrick
中科院分区：
文献类型：
--
作者：
Hennig, Martin;Grafinger, Manfred;Rosenberger, Patrick
机器学习方法被认为是改善制造业操作和流程的一种有前途的方法。然而，机器学习的应用通常需要数据科学家的专业知识以及对制造过程的全面了解。专注于某些高附加值、变体丰富的生产流程的中小型公司通常缺乏内部数据科学家，因此错过了从生产数据流中生成更深入的数据驱动洞察力的机会。本文提出了一种三步机器学习方法，以授权机器学习知识有限的过程专家：1)通过聚类进行数据探索，2)通过特殊结构的神经网络表示生产系统行为，3)通过进化算法查询这种表示，通过在线优化或场景模拟实现决策支持。所选择的算法侧重于参数轻，完善，通用的算法，以降低知识要求，为他们的应用。
Machine learning methods are considered a promising approach for improving operations and processes in manufacturing. However, the application of machine learning often requires the expertise of a data scientist combined with thorough knowledge of the manufacturing processes. Small and medium-sized companies that specialize in certain high value-added, variant rich production processes often lack an in-house data scientist and therefore miss out on generating a deeper data driven insight from their production data streams. This paper proposes a three-step machine learning methodology to empower process experts with limited knowledge in machine learning: 1) data exploration through clustering, 2) representation of the production systems behaviour through specially structured neural networks and 3) querying this representation through evolutionary algorithms to achieve decision support through online optimization or scenario simulation. The chosen algorithms focus on parameter-light, well-established, general use algorithms in order to lower knowledge requirements for their application.