Multisource Elastic Meta-Transfer Learning for Few-Shot Fault Diagnosis of Aero-Engine Under Unstable Speed

Engine health management is a critical technology for enhancing the safety, reliability, and economic efficiency of aero-engines. However, the severe scarcity of fault data presents significant challenges for intelligent fault diagnosis of inter-shaft bearings in aero-engines. Existing research on few-shot fault diagnosis of aero-engines have primarily focused on constant-speed conditions, without fully considering the speed fluctuations during aircraft takeoff and landing. To address these challenges, this study proposes a Multi-source Elastic Meta-Transfer Learning (MEMTL) method for few-shot cross-domain fault diagnosis of aero-engine inter-shaft bearings under unstable speed conditions. First, to handle the non-stationary nature of signals under variable-speed conditions, a wavelet attention network is designed for feature extraction under unstable speed conditions. This method employs continuous wavelet transform for time-frequency feature extraction from vibration signals, and integrates hybrid attention modules—channel attention and spatial attention—to adaptively capture multi-scale features across different frequency bands, thereby improving the detection of subtle fault signatures. Second, to address the information deficiency caused by limited fault data, a multi-source elastic relation network is developed. This model incorporates data from multiple source domains into the training process, fully leveraging the diversity of information from different operating conditions. To effectively deal with potential feature distribution discrepancies across source domains, a transfer learning strategy based on inter-domain elastic factors is introduced. This strategy dynamically adjusts the contribution of each source domain during training to reduce negative transfer effects in cross-domain learning. Comparative experiments with state-of-the-art methods demonstrate that the proposed method achieves higher accuracy, more stable performance, and stronger adaptability for few-shot cross-domain fault diagnosis under unstable speed conditions.