# AeroGPT: Leveraging Large-Scale Audio Model for Aero-Engine Bearing Fault Diagnosis

arXiv: https://arxiv.org/abs/2506.16225 · HTML: https://mlsanity.com/paper/2506.16225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [InsightX Agent: An LMM-based Agentic Framework with Integrated Tools for Reliable X-ray NDT Analysis](https://mlsanity.com/paper/2507.14899/) (2025)
- [AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow](https://mlsanity.com/paper/2604.11945/) (2026)

## Abstract (arXiv)

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.