【Applied Energy 最新原创论文】基于标签数据监督微调的暖通空调系统故障诊断大语言模型

•提出了一种基于标签数据监督的大语言模型微调方法。

•开发了一种基于自校正的大语言模型微调数据集生成方法。

•设计了一种用于大语言模型微调数据集动态更新的数据增强方法。

•微调后的大语言模型对空气处理机组故障的诊断精度接近100 %。

•微调后的大语言模型具备强大的泛化能力，并且对输入维度的变化不敏感。

Large language models (LLMs) have exhibited great potential in fault diagnosis of heating, ventilation, and air conditioning systems. However, the fault diagnosis accuracy of LLMs is still unsatisfactory, due to the lack of effective diagnosis accuracy enhancement methods for LLMs. To fill this gap, this study proposes a LLM fine-tuning method supervised by data with fault and fault-free labels to enhance the fault diagnosis accuracy of LLMs. This method designs a LLM self-correction strategy to automatically generate a fine-tuning dataset based on the labeled data. The generated fine-tuning dataset is applied to fine-tune a LLM. Moreover, a data augmentation-based approach is put forward to adaptively update the fine-tuning dataset for iteratively developing a high-performance fine-tuned LLM. The proposed method is utilized to fine-tune the GPT-3.5 model using the air handling unit (AHU) fault dataset from the RP-1312 project. The results show that the diagnosis accuracy of the GPT-3.5 model is increased from 29.5 % to 100.0 % after model fine-tuning. Compared with the GPT-4 model, the fine-tuned GPT-3.5 model achieves a 31.1 % higher average diagnosis accuracy. The fine-tuned GPT-3.5 model is also applied to diagnose faults in two AHUs from another open-source dataset to verify the generalization ability of this model. The two AHUs have different system structures and sensor configurations compared to the AHU in the RP-1312 dataset, and this dataset is not utilized to fine-tune the GPT-3.5 model. The average diagnosis accuracy of the GPT-3.5 model is increased from 46.0 % to 99.1 % and from 38.8 % to 98.9 % for the faults in the two AHUs, respectively, after model fine-tuning. Furthermore, the proposed method is verified using two fault datasets from a variable air volume box and a chiller plant system. After fine-tuning the GPT-3.5 model using the two datasets, the average diagnosis accuracy of this model is increased from 33.0 % to 98.3 % for variable air volume box faults and from 36.0 % to 99.1 % for chiller plant system faults. This study provides an effective solution to the development of domain-specific LLMs for this domain.

Keywords

Large language models；

Generative pre-trained transformers (GPT)；

Large language model fine-tuning；

Fault diagnosis；

Heating, ventilation and air conditioning systems；

图1. 暖通空调系统故障诊断专用大语言模型的开发与部署流程图

图2. 大语言模型自校正流程图

图3. GPT-3.5模型、GPT-4模型以及经过微调的GPT-3.5模型在RP-1312空气处理机组故障数据集上的诊断精度（微调采用RP-1312数据集）

图4. GPT-3.5模型、GPT-4模型以及经过微调的GPT-3.5模型在LBNL空气处理机组故障数据集上的诊断精度（微调采用RP-1312数据集，LBNL数据集未用于模型微调）

图5. 微调后的GPT-3.5模型对“排气阀门卡死在全开位置”故障的回复示例

       本研究由浙江大学、荷兰Eindhoven University of Technology、以及英国University of Cambridge的研究人员共同完成。

通信作者简介：

       章超波，荷兰Eindhoven University of Technology博士后。主要从事基于人工智能算法的建筑能源系统大数据分析、故障诊断、优化控制和仿真建模研究。在Applied Energy、Automation in Construction、Building and Environment和Energy and Buildings等期刊上发表论文40余篇，谷歌学术累计被引1600余次。曾获Energy and Built Environment期刊2020 Best Paper、该期刊学术新人奖提名，以及科爱十年百篇优秀论文等奖项。

第一作者简介：

      张健，浙江大学能源工程学院博士研究生。主要从事结合大语言模型的能源系统大数据分析和故障诊断研究。

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