Attention-Aware Meta-Reweighted Optimization for Enhanced Intelligent Fault Diagnosis

Due to stringent aircraft safety requirements and the high cost of experiments, there is a scarcity of failure samples, creating a gap between existing diagnostic models and practical applications. To address this issue, we have developed a small-sample civil aircraft fault diagnosis method, which hybridizes a meta-learning approach to deal with data imbalance and a channel attention mechanism to enhance feature extraction efficiency. Specifically, our approach integrates the advantages of meta-learning and attention regularization, effectively addressing both the imbalance in training sample distribution and the need for human interaction in enhancing feature representation. We then considered five data imbalances and introduced a fault diagnosis algorithm based on a one-dimensional convolutional network, which is well applied in solving the small samples yield tasks within two datasets. Additionally, we provide baseline accuracy under the same conditions for a comprehensive comparison and reference. Through elaborated experiments, our method achieves competitive performance and demonstrates its superiority in solving imbalanced distribution experimental configurations.