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首页> 外文会议>IEEE International Conference on Automation Science and Engineering >Data-and Expert-Driven Analysis of Cause-Effect Relationships in the Production of Lithium-Ion Batteries
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Data-and Expert-Driven Analysis of Cause-Effect Relationships in the Production of Lithium-Ion Batteries

机译：锂离子电池生产中因果关系的数据和专家驱动分析

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The development of lithium-ion batteries (LIBs) is characterized by a unique level of complexity in the manufacturing process. In particular, cause-effect relationships (CERs) between process parameters have a strong influence on the quality of a manufactured cell and thus on the ramp-up time. First approaches for discovery CERs in LIBs were expert-based and thus afflicted with a high degree of uncertainty. Therefore, data from a real battery production line has for the first time been systematically processed and analyzed using CRISP-DM. However, the approach shows shortcomings in the involvement of domain expert knowledge as well as in the accuracy of the applied models. Addressing these shortcomings, an interdisciplinary data analytics framework is presented using human-computer interaction (HCI). Moreover, the framework aims to improve data analysis with the help of expert knowledge and, conversely, sharpen the knowledge of experts through data analysis. Thus, the model provides a basis for automated fault detection, diagnostics, and prognostics. Implementation and validation of the framework was conducted using the data of an assembly line for prismatic LIBs at the BMW Group in Munich.
机译：锂离子电池（LIB）的发展特点是制造过程具有独特的复杂性。特别地，过程参数之间的因果关系（CER）对制造的电池的质量有很大的影响，因此对加速时间也有很大的影响。在LIB中发现CER的第一种方法是基于专家的，因此受到高度不确定性的困扰。因此，第一次使用CRISP-DM对来自真实电池生产线的数据进行了系统地处理和分析。但是，该方法在领域专家知识的参与以及所应用模型的准确性方面显示出缺点。针对这些缺点，提出了使用人机交互（HCI）的跨学科数据分析框架。此外，该框架旨在借助专家知识来改善数据分析，反之，则通过数据分析来增强专家的知识。因此，该模型为自动故障检测，诊断和预测提供了基础。该框架的实施和验证是使用慕尼黑宝马集团棱柱形LIB装配线的数据进行的。

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