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Integrating large language models for improved failure mode and effects analysis (FMEA): a framework and case study

Published online by Cambridge University Press:  16 May 2024

Ibtissam El Hassani ,
Tawfik Masrour ,
Nouhan Kourouma ,
Damien Motte  and
Jože Tavčar
Ibtissam El Hassani
Affiliation:
Moulay Ismail University, Morocco University of Quebec at Rimouski, Canada
Tawfik Masrour
Affiliation:
Moulay Ismail University, Morocco University of Quebec at Rimouski, Canada
Nouhan Kourouma
Affiliation:
Moulay Ismail University, Morocco
Damien Motte
Affiliation:
Lund University, Sweden
Jože Tavčar*
Affiliation:
Lund University, Sweden
*
joze.tavcar@design.lth.se

Abstract

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The manual execution of failure mode and effects analysis (FMEA) is time-consuming and error-prone. This article presents an approach in which large language models (LLMs) are integrated into FMEA. LLMs improve and accelerate FMEA with human in the loop. The discussion looks at software tools for FMEA and emphasizes that the tools must be tailored to the needs of the company. Our framework combines data collection, pre-processing and reliability assessment to automate FMEA. A case study validates this framework and demonstrates its efficiency and accuracy compared to manual FMEA.

Keywords

failure mode and effect analysis (FMEA)large language model (LLM)generative AIproduct qualityknowledge management
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2019 - 2028
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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