Towards Automated Solution Recipe Generation for Industrial Asset Management with LLM

TL;DR

This paper presents an innovative approach using Large Language Models and taxonomy-guided prompting to automate the creation of solution recipes for Industrial Asset Management, reducing human effort and improving automation.

Contribution

It introduces a novel taxonomy-guided prompting method and LLM pipelines for automatic solution recipe generation in IAM, minimizing domain expertise reliance.

Findings

Effective asset health assessment across ten asset classes

Demonstrated automation of solution recipe generation

Potential for rapid deployment in industrial settings

Abstract

This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Language Models (LLMs). Our research introduces an automated model-building process, traditionally reliant on intensive collaboration between data scientists and domain experts. We present two primary innovations: a taxonomy-guided prompting generation that facilitates the automatic creation of AI solution recipes and a set of LLM pipelines designed to produce a solution recipe containing a set of artifacts composed of documents, sample data, and models for IAM. These pipelines, guided by standardized principles, enable the generation of initial solution templates for heterogeneous asset classes without direct human input, reducing reliance on extensive domain knowledge and enhancing automation. We evaluate our methodology by assessing asset health and sustainability across a spectrum of ten asset classes. Our findings illustrate the potential of LLMs and taxonomy-based LLM prompting pipelines in transforming asset management, offering a blueprint for subsequent research and development initiatives to be integrated into a rapid client solution.