FMEA Builder: Expert Guided Text Generation for Equipment Maintenance
This addresses the need for efficient document creation in equipment maintenance for reliability professionals, but it is incremental as it applies existing foundation models to a specific domain task.
The paper tackles the problem of generating structured Failure Mode and Effects Analysis (FMEA) documents for equipment maintenance by using large language models, with results showing that foundation models can correctly generate over half of the key content and positive feedback from reliability professionals.
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.