Integrating large language models for improved failure mode and effects analysis (FMEA) : a framework and case study
(2024) 18th International Design Conference, DESIGN 2024 In Proceedings of the Design Society 4. p.2019-2028- Abstract
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7ffa6f5a-a076-4f36-a3a3-2be834489485
- author
- El Hassani, Ibtissam
; Masrour, Tawfik
; Kourouma, Nouhan
; Motte, Damien
LU
and Tavčar, Jože LU
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Failure mode and effect analysis, FMEA, Large language models, LLM, Generative AI, Product quality, Knowledge management
- host publication
- Proceedings of the Design Society. International Design Conference, DESIGN 2024 20-24 May 2024, Dubrovnik, Croatia
- series title
- Proceedings of the Design Society
- volume
- 4
- pages
- 2019 - 2028
- publisher
- Cambridge University Press
- conference name
- 18th International Design Conference, DESIGN 2024
- conference location
- Dubrovnik, Croatia
- conference dates
- 2024-05-20 - 2024-05-24
- external identifiers
-
- scopus:85194055333
- ISSN
- 2732-527X
- DOI
- 10.1017/pds.2024.204
- language
- English
- LU publication?
- yes
- id
- 7ffa6f5a-a076-4f36-a3a3-2be834489485
- date added to LUP
- 2024-05-25 15:45:35
- date last changed
- 2024-06-14 15:03:29
@inproceedings{7ffa6f5a-a076-4f36-a3a3-2be834489485, abstract = {{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.}}, author = {{El Hassani, Ibtissam and Masrour, Tawfik and Kourouma, Nouhan and Motte, Damien and Tavčar, Jože}}, booktitle = {{Proceedings of the Design Society. International Design Conference, DESIGN 2024 20-24 May 2024, Dubrovnik, Croatia}}, issn = {{2732-527X}}, keywords = {{Failure mode and effect analysis; FMEA; Large language models; LLM; Generative AI; Product quality; Knowledge management}}, language = {{eng}}, pages = {{2019--2028}}, publisher = {{Cambridge University Press}}, series = {{Proceedings of the Design Society}}, title = {{Integrating large language models for improved failure mode and effects analysis (FMEA) : a framework and case study}}, url = {{https://lup.lub.lu.se/search/files/187654730/ElHassani_al_Design2024.pdf}}, doi = {{10.1017/pds.2024.204}}, volume = {{4}}, year = {{2024}}, }