AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis
提出AeroGPT框架，将大规模音频模型知识迁移到航空发动机轴承故障诊断，通过振动信号对齐和生成式故障分类直接输出可解释的故障标签，在两个数据集上分别达到98.94%和100%的准确率。
Aerospace engines, as critical components in the aviation and aerospace industries, require continuous and accurate fault diagnosis to ensure operational safety and prevent catastrophic failures. While deep learning techniques have been extensively studied in this context, they typically output logits or confidence scores, necessitating postprocessing to obtain actionable insights. Furthermore, the potential of large-scale audio models for this task remains largely untapped. To address these limitations, this article proposes AeroGPT, a novel framework that transfers knowledge from the general audio domain to aero-engine bearing fault diagnosis. AeroGPT leverages a large-scale audio model and incorporates vibration signal alignment (VSA) to adapt general audio knowledge to domain-specific vibration patterns, along with generative fault classification (GFC) to directly generate interpretable fault labels. This approach eliminates the need for label postprocessing and supports interactive, interpretable, and actionable fault diagnosis, thereby enhancing industrial applicability. Through comprehensive experimental validation on two aero-engine bearing datasets, AeroGPT achieves 98.94% accuracy on the Dynamic and Identification Research Group (DIRG) dataset and 100% accuracy on Harbin Institute of Technology (HIT) bearing dataset, outperforming representative deep learning approaches. Qualitative analysis and further discussion also demonstrate its potential for interactive diagnosis and real-world deployment, highlighting the promise of large-scale audio models to advance fault diagnosis in aerospace applications.