论文标题
用基于FMEA的大规模贝叶斯网络生产锂离子电池的根本原因分析
Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network
论文作者
论文摘要
锂离子电池电池的产生的特征是由于过程特征之间的许多因果关系，因此具有高度的复杂性。关于多阶段生产的知识在几位专家之间传播，将任务作为失败分析挑战。在本文中，提出了一种新的方法，其中包括通过将故障模式和效果分析（FMEA）与贝叶斯网络相结合的生产中的专家知识获取。提出了特殊的算法，这些算法有助于检测和解决专家提供的参数之间的不一致之处，这些参数在从几个过程专家那里收集知识时必定会发生。我们通过在锂离子电池生产中建立大规模的跨加工贝叶斯故障网络及其用于根本原因分析的应用来展示这种整体方法的有效性。
The production of lithium-ion battery cells is characterized by a high degree of complexity due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks as failure analysis challenging. In this paper, a new method is presented that includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. Special algorithms are presented that help detect and resolve inconsistencies between the expert-provided parameters which are bound to occur when collecting knowledge from several process experts. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production and its application for root cause analysis.
