arXiv:2606.14119v1 Announce Type: new Abstract: Fault diagnostics and recovery in smart factories is challenging because critical information is dispersed across manuals of multiple machines which are interconnected through the manufacturing process. Large Language Models (LLMs) can provide a promising approach. In this paper, we propose FactoryLLM, a safe and open-source AI playground designed for evaluating different LLM-based retrieval-augmented generation (RAG) models by analysing documents from multiple machines across the manufacturing process. FactoryLLM enables the user to configure th
The increasing complexity of smart factories and the rapid advancement of LLM capabilities are converging to make AI-driven fault diagnostics a critical area of development.
This development indicates a tangible application of LLMs beyond general text generation, directly impacting the efficiency and maintenance of industrial processes.
The ability to use LLMs to consolidate and analyze disparate technical manuals across multiple machines facilitates more effective fault diagnosis and recovery in manufacturing.
- · Smart factory operators
- · LLM developers
- · Industrial AI solution providers
- · Traditional manual diagnostic services
Improved operational efficiency and reduced downtime in smart factories due to faster fault resolution.
Increased demand for specialized industrial LLMs and RAG models tailored for manufacturing environments.
The integration of AI-powered diagnostic tools becomes a standard feature in future industrial machinery design.
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Read at arXiv cs.AI