Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network

Michael Kirchhof*, Klaus Haas, Thomas Kornas, Sebastian Thiede, Mario Hirz, Christoph Herrmann

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

130 Downloads (Pure)

Abstract

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.
Original languageEnglish
Number of pages18
JournalCIRP journal of manufacturing science and technology
Publication statusE-pub ahead of print/First online - 5 Jun 2020
Externally publishedYes

Keywords

  • Bayesian networks
  • Root cause analysis
  • Failure mode and effect analysis
  • Lithium-ion battery
  • Multi-stage production
  • Manufacturing processes
  • Optimization
  • Consistency

Fingerprint

Dive into the research topics of 'Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network'. Together they form a unique fingerprint.

Cite this