Enhanced Fault Diagnosis Using Large Language Models and Probabilistic Label Fusion
Year: 2025
Type: article
Abstract: Fault diagnosis is crucial for ensuring the reliability of manufacturing systems. However, a large proportion of unlabeled data remains unused, which presents a challenge that limits fault diagnosis performance. While some semi-supervised methods focus on leveraging unlabeled data, they rely solely on statistical properties and lack domain knowledge, leading to performance bottlenecks. In this paper, we propose a fault diagnosis framework that leverages Large Language Models (LLMs), which contain vast amounts of prior knowledge, to enhance Classification performance. We train a base classifier on labeled data, generate pseudo-labels for unlabeled data, and fine-tune LLaMA3 to infer another set of pseudo-labels. By merging these labels and adjusting sample weights, we create an augmented training set, which improves model performance. Our approach outperforms traditional methods as demonstrated on the Tennessee Eastman Process dataset.
Source: IFAC-PapersOnLine
Authors: Qunlong Chen, Shouchen Yao, Peixiang Wang, Jiayu Shi, Wei Qin, Jing Li, Jian Wang, Weiwei He
Institution: Shanghai Jiao Tong University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414567901