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

TL;DR

Aerospace engines, as critical components in aviation and aerospace industries, require continuous and accurate fault diagnosis to ensure operational safety and prevent catastrophic failures and this paper proposes AeroGPT, a novel framework that transfers knowledge from the general audio domain to aero-engine bearing fault diagnosis.

Abstract

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.

Figures (4)

• Figure 1: Overall framework of AeroGPT and its practical application to aero-engine bearing fault diagnosis.
• Figure 2: Technical components of the AeroGPT methodology. The framework is initialized with a foundation model, followed by Vibration Signal Alignment Stage to adapt general audio knowledge to domain-specific vibration patterns and Generative Fault Classification Stage to output interpretable fault labels.
• Figure 3: Examples of AeroGPT's generative fault diagnosis capability and answers to follow-up queries.
• Figure 4: Comparison of AeroGPT's generative fault diagnosis ability with general-purpose models and conventional approaches.