Human-Like One-Shot Fault Diagnosis via Large Language Model
将传感器数据转为语言描述，利用大语言模型推理引入先验知识，以信息距离为学习度量，实现零错误的类人一次性故障诊断，适用于复杂工业系统。
The few-shot fault diagnosis methods have achieved competitive results in complex systems, but existing approaches fail to achieve human-like one-shot diagnostic learning. This work uses the large language model (LLM) to enhance fault diagnosis with human-like reasoning. First, we transform sensor data into language-based representations for more detailed feature descriptions. Then, we introduce prior knowledge into the language-based diagnostic data through the inference process of the LLM to achieve information gain. Furthermore, we apply arithmetic coding to encode predictive probabilities, forming an entropy-based compressor approximating the information distance derived from Kolmogorov complexity. We achieve the one-shot fault diagnosis by using information distance as the learning metric for human-like few-shot learning. Experimental results on chiller systems show that our method, empowered by the LLM, enables zero-error human-like fault diagnosis. The proposed method holds promise for significantly enhancing intelligent fault perception and maintenance capabilities in complex industrial systems, while also offering a novel research paradigm for the application of LLMs in industrial fault diagnosis.