Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.17834 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914050910191616 |
|---|---|
| author | Kabakci-Zorlu, Duygu Lorenzi, Fabio Sheehan, John Lynch, Karol Eck, Bradley |
| author_facet | Kabakci-Zorlu, Duygu Lorenzi, Fabio Sheehan, John Lynch, Karol Eck, Bradley |
| contents | We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment. Our system aggregates unstructured content across documents to generate an FMEA and stores it in a relational database. Leveraging this tool, the time required for creation of this knowledge-intensive content is reduced, outperforming traditional manual approaches. This demonstration showcases the potential of foundation models to facilitate the creation of specialized structured content for enterprise asset management systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Documents to Database: Failure Modes for Industrial Assets Kabakci-Zorlu, Duygu Lorenzi, Fabio Sheehan, John Lynch, Karol Eck, Bradley Databases Artificial Intelligence Computation and Language We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment. Our system aggregates unstructured content across documents to generate an FMEA and stores it in a relational database. Leveraging this tool, the time required for creation of this knowledge-intensive content is reduced, outperforming traditional manual approaches. This demonstration showcases the potential of foundation models to facilitate the creation of specialized structured content for enterprise asset management systems. |
| title | From Documents to Database: Failure Modes for Industrial Assets |
| topic | Databases Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.17834 |