Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models

TL;DR

This paper proposes a five-layer framework that combines large language models with small domain-specific models to improve industrial maintenance efficiency, accuracy, and real-time performance.

Contribution

It introduces a novel collaborative fusion framework integrating large and small models for industrial maintenance, addressing domain adaptability and knowledge fusion challenges.

Findings

Framework enhances maintenance efficiency in real-world scenarios

Combines reasoning and knowledge integration of large models with precision of small models

Demonstrates significant improvements in industrial maintenance tasks

Abstract

With the rapid advancement of intelligent technologies, collaborative frameworks integrating large and small models have emerged as a promising approach for enhancing industrial maintenance. However, several challenges persist, including limited domain adaptability, insufficient real-time performance and reliability, high integration complexity, and difficulties in knowledge representation and fusion. To address these issues, an intelligent maintenance framework for industrial scenarios is proposed. This framework adopts a five-layer architecture and integrates the precise computational capabilities of domain-specific small models with the cognitive reasoning, knowledge integration, and interactive functionalities of large language models. The objective is to achieve more accurate, intelligent, and efficient maintenance in industrial applications. Two realistic implementations, involving the maintenance of telecommunication equipment rooms and the intelligent servicing of energy storage power stations, demonstrate that the framework significantly enhances maintenance efficiency.