Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.05054 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916472935153664 |
|---|---|
| author | Lynch, Karol Lorenzi, Fabio Sheehan, John Kabakci-Zorlu, Duygu Eck, Bradley |
| author_facet | Lynch, Karol Lorenzi, Fabio Sheehan, John Kabakci-Zorlu, Duygu Eck, Bradley |
| contents | Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets. A Failure Mode and Effects Analysis (FMEA) captures the composition of an asset or piece of equipment, the ways it may fail and the consequences thereof. Our system uses large language models to enable fast and expert supervised generation of new FMEA documents. Empirical analysis shows that foundation models can correctly generate over half of an FMEA's key content. Results from polling audiences of reliability professionals show a positive outlook on using generative AI to create these documents for critical assets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FMEA Builder: Expert Guided Text Generation for Equipment Maintenance Lynch, Karol Lorenzi, Fabio Sheehan, John Kabakci-Zorlu, Duygu Eck, Bradley Computation and Language Artificial Intelligence Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets. A Failure Mode and Effects Analysis (FMEA) captures the composition of an asset or piece of equipment, the ways it may fail and the consequences thereof. Our system uses large language models to enable fast and expert supervised generation of new FMEA documents. Empirical analysis shows that foundation models can correctly generate over half of an FMEA's key content. Results from polling audiences of reliability professionals show a positive outlook on using generative AI to create these documents for critical assets. |
| title | FMEA Builder: Expert Guided Text Generation for Equipment Maintenance |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.05054 |