  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.
