Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning

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.