融合大模型与图神经网络的电力设备缺陷诊断

doi:10.3778/j.issn.1673-9418.2405085

摘要/Abstract

摘要： 电力系统中不同装置设备的缺陷评级和分析处理常受运维人员主观性影响，导致同一缺陷文本描述出现不同的严重程度评级。专业知识的差异也导致诊断分析差异和诊断效率不同。为提升缺陷诊断的准确性和效率，提出一种基于图神经网络的缺陷文本评级分类方法和大模型智能诊断分析助手。构建专业词典，使用自然语言处理算法规范化文本描述。通过统计方法，优化缺陷文本的语义表示。集成图注意力神经网络和RoBERTa模型对缺陷文本进行精确评级分类。基于大语言模型Qwen1.5-14B-Chat进行低秩适配（LoRA）微调训练得到电力设备诊断大模型Qwen-ElecDiag，结合检索增强生成技术开发设备缺陷诊断助手。此外，整理提供微调电力设备诊断大模型的指令数据集。对比实验结果表明，提出的基于图神经网络的缺陷评级分类方法在准确性上较最优基准模型BERT提升近8个百分点；诊断助手的电力知识以及缺陷诊断能力得到提升。通过提高缺陷评级的准确率并提供全面专业化诊断建议，不仅提高电力设备运维的智能化水平，也为其他垂直领域的智能运维提供新的解决方案。

关键词: 电力系统, 缺陷诊断, 图神经网络, 大语言模型, 低秩适配（LoRA）微调, 检索增强生成, 智能运维

Abstract: Defect ratings and analysis and processing of different devices and equipment in the power system are often affected by the subjectivity of operation and maintenance personnel, resulting in different severity ratings for the same defect text description. Differences in expertise also lead to differences in diagnostic analysis and different diagnostic efficiency. In order to improve the accuracy and efficiency of defect diagnosis, a defect text rating classification method based on graph neural network and a large model intelligent diagnosis and analysis assistant are proposed. Firstly, a professional dictionary is constructed to normalize the text description using natural language processing algorithms. Secondly, the semantic representation of defective text is optimized by statistical methods. Then, graph attention neural network and robustly optimized BERT approach (RoBERTa) are integrated to accurately rate and classify defective text. Finally, low-rank adaptation (LoRA) fine-tuning training based on the large language model Qwen1.5-14B-Chat is performed to obtain the large model Qwen-ElecDiag for power equipment diagnosis, which is combined with retrieval enhancement to generate the assistant for defect diagnosis of technology development equipment. In addition, the collation provides the instruction dataset for fine-tuning the power equipment diagnosis macromodel. Comparative experimental results show that the proposed graph neural network-based defect rating classification method improves nearly 8 percentage points in accuracy over the optimal baseline model BERT; the diagnostic assistant??s power knowledge as well as defect diagnostic capability is improved. By improving the accuracy of defect ratings and providing comprehensive specialized diagnostic suggestions, it not only improves the intelligent level of power equipment O&M, but also provides new solutions for intelligent O&M in other vertical fields.

Key words: power system, defect diagnosis, graph neural networks, large language model, low-rank adaptation (LoRA) fine-tuning, retrieval-augmented generation, intelligent operation and maintenance

李莉, 时榕良, 郭旭, 蒋洪鑫. 融合大模型与图神经网络的电力设备缺陷诊断[J]. 计算机科学与探索, 2024, 18(10): 2643-2655.

LI Li, SHI Rongliang, GUO Xu, JIANG Hongxin. Diagnosis of Power System Defects by Large Language Models and Graph Neural Networks[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(10): 2643-2655.

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