Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918047386697728 |
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| author | Yuan, Xiaoyi Huang, Qiming Guo, Mingqing Ma, Huiming Xu, Ming Liu, Zeyi He, Xiao |
| author_facet | Yuan, Xiaoyi Huang, Qiming Guo, Mingqing Ma, Huiming Xu, Ming Liu, Zeyi He, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models Yuan, Xiaoyi Huang, Qiming Guo, Mingqing Ma, Huiming Xu, Ming Liu, Zeyi He, Xiao Systems and Control 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. |
| title | Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.05854 |