Aerospace engines, as critical components in 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 post-processing to obtain actionable insights. Furthermore, the potential of large-scale audio models for this task remains largely untapped. To address these limitations, this paper 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 post-processing 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 DIRG dataset and 100% accuracy on the 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.

翻译：航空发动机作为航空航天工业的关键部件，需要持续且准确的故障诊断以确保运行安全并预防灾难性故障。尽管深度学习技术在此背景下已得到广泛研究，但其通常输出逻辑值或置信度分数，需要后处理才能获得可操作的见解。此外，大规模音频模型在此任务中的潜力在很大程度上尚未被开发。为应对这些局限性，本文提出AeroGPT，一种将通用音频领域知识迁移至航空发动机轴承故障诊断的新型框架。AeroGPT利用大规模音频模型，并引入振动信号对齐（VSA）将通用音频知识适配至领域特定的振动模式，同时结合生成式故障分类（GFC）直接生成可解释的故障标签。该方法消除了标签后处理的需求，并支持交互式、可解释且可操作的故障诊断，从而提升了工业适用性。通过在两个航空发动机轴承数据集上的全面实验验证，AeroGPT在DIRG数据集上达到了98.94%的准确率，在HIT轴承数据集上达到了100%的准确率，优于代表性的深度学习方法。定性分析及进一步讨论也证明了其在交互式诊断和实际部署方面的潜力，凸显了大规模音频模型在推动航空航天应用故障诊断发展中的前景。