A multimodal expert system for the intelligent monitoring and maintenance of transformers enhanced by multimodal language large model fine-tuning and digital twins

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2024-11-28 DOI:10.1049/cim2.70007
Xuedong Zhang, Wenlei Sun, Ke Chen, Renben Jiang
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Abstract

The development of multimodal large models and digital twin technology is set to revolutionise the methods of intelligent monitoring and maintenance for transformers. To address the issues of low intelligence level, single application mode, and poor human–machine collaboration in traditional transformer monitoring and maintenance methods, an intelligent monitoring and maintenance digital twin multimodal expert reasoning system, fine-tuned on visual language-based large models, is proposed. The paper explores the modes and methods for implementing intelligent monitoring and maintenance of transformers based on multimodal data, large models, and digital twin technology. A multimodal language large model (MLLM) framework for intelligent transformer maintenance, grounded on the Large Language and Vision Assistant model, has been designed. To enable large models to understand and reason about image annotation areas, an adaptive grid-based positional information processor has been designed. To facilitate the compatibility and learning of large models with transformer Dissolved Gas Analysis data, a heterogeneous modality converter based on the Gram–Schmidt angular field has been developed. For the unified modelling and management of multimodal reasoning and comprehensive resource integration in human–machine dialogue, a central linker based on an identity resolution asset management shell has been designed. Subsequently, a visual-language multimodal dataset for transformer monitoring and maintenance was constructed. Finally, by fine-tuning parameters, a multimodal expert reasoning system for intelligent transformer monitoring and maintenance was developed. This system not only achieves real-time monitoring of the transformer's operational status but also generates maintenance strategies intelligently based on operational conditions. The expert system possesses robust human–machine dialogue capabilities and reasoning generation abilities. This research provides a reference for the deep integration of MLLM and digital twin in industrial scenarios, particularly in the application modes of intelligent operation and maintenance for transformers.

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基于多模态语言、大模型微调和数字孪生技术的变压器智能监测与维护专家系统
多式联运大型模型和数字孪生技术的发展将彻底改变变压器的智能监测和维护方法。针对传统变压器监测维护方法中存在的智能水平低、应用模式单一、人机协作能力差等问题,提出了一种基于视觉语言的大模型微调的智能监测维护数字孪生多模态专家推理系统。探讨了基于多模态数据、大模型和数字孪生技术的变压器智能监测与维护的模式和方法。在大语言和视觉辅助模型的基础上,设计了一个面向智能变压器维护的多模态语言大模型(MLLM)框架。为了使大型模型能够理解和推理图像标注区域,设计了一种基于自适应网格的位置信息处理器。为了便于大型模型与变压器溶解气体分析数据的兼容和学习,开发了一种基于Gram-Schmidt角场的异构模态转换器。为了实现人机对话中多模态推理的统一建模和管理以及资源的综合集成,设计了基于身份解析资产管理外壳的中心链接器。在此基础上,构建了用于变压器监测与维护的可视化语言多模态数据集。最后,通过对参数进行微调,开发了智能变压器监测与维护的多模态专家推理系统。该系统不仅实现了对变压器运行状态的实时监控,还能根据运行情况智能地生成维护策略。专家系统具有强大的人机对话能力和推理生成能力。本研究为工业场景下MLLM与数字孪生的深度融合,特别是变压器智能运维的应用模式提供了参考。
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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
期刊最新文献
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