Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge
  Base for Industrial Prognostics and Health Management
Huan Wang Yan-Fu Li Min Xie
Prognostics and health management (PHM) is essential for industrial operation
and maintenance, focusing on predicting, diagnosing, and managing the health
status of industrial systems. The emergence of the ChatGPT-Like large-scale
language model (LLM) has begun to lead a new round of innovation in the AI
field. It has extensively promoted the level of intelligence in various fields.
Therefore, it is also expected further to change the application paradigm in
industrial PHM and promote PHM to become intelligent. Although ChatGPT-Like
LLMs have rich knowledge reserves and powerful language understanding and
generation capabilities, they lack domain-specific expertise, significantly
limiting their practicability in PHM applications. To this end, this study
explores the ChatGPT-Like LLM empowered by the local knowledge base (LKB) in
industrial PHM to solve the above limitations. In addition, we introduce the
method and steps of combining the LKB with LLMs, including LKB preparation, LKB
vectorization, prompt engineering, etc. Experimental analysis of real cases
shows that combining the LKB with ChatGPT-Like LLM can significantly improve
its performance and make ChatGPT-Like LLMs more accurate, relevant, and able to
provide more insightful information. This can promote the development of
ChatGPT-Like LLMs in industrial PHM and promote their efficiency and quality.