From Documents to Database: Failure Modes for Industrial Assets
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914050910191616 |
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| 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 |