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

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 journal › Article › Academic › peer-review

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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 language	English
Number of pages	18
Journal	CIRP journal of manufacturing science and technology
Publication status	E-pub ahead of print/First online - 5 Jun 2020
Externally published	Yes

Keywords

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

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Kirchhof2020rootSubmitted version, 1.4 MBLicence: CC BY-NC-SA

https://arxiv.org/abs/2006.03610Licence: CC BY-NC-SA

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

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. ",

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author = "Michael Kirchhof and Klaus Haas and Thomas Kornas and Sebastian Thiede and Mario Hirz and Christoph Herrmann",

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language = "English",

journal = "CIRP journal of manufacturing science and technology",

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T1 - Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network

AU - Kirchhof, Michael

AU - Haas, Klaus

AU - Kornas, Thomas

AU - Thiede, Sebastian

AU - Hirz, Mario

AU - Herrmann, Christoph

N1 - Submitted to CIRP Journal of Manufacturing Science and Technology (01.2020)

PY - 2020/6/5

Y1 - 2020/6/5

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - Root cause analysis

KW - Failure mode and effect analysis

KW - Lithium-ion battery

KW - Multi-stage production

KW - Manufacturing processes

KW - Optimization

KW - Consistency

UR - https://research.utwente.nl/en/publications/root-cause-analysis-in-lithiumion-battery-production-with-fmeabased-largescale-bayesian-network(01f71ce8-998a-44c0-8419-a4ba637ae2a1).html

M3 - Article

JO - CIRP journal of manufacturing science and technology

JF - CIRP journal of manufacturing science and technology

SN - 1755-5817

ER -

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

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Keywords

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