A few-shot fault diagnosis method for aircraft engine casing

Abstract

Abstract In recent years, the label monitoring data collected from aeroengine accessory casing system are limited, and there are differences between the training data domain and the target data domain in practical application, which leads to the degradation of the model diagnosis performance. To solve the above problems, a few-shot fault diagnosis method based on meta-learning is proposed in this paper. Firstly, the original signal is converted into time-frequency image, and the multi-channel time-frequency image is fused as the input of the network model. Secondly, an autoencoder is trained using the source data domain, and the encoder is used as the feature extraction network. Thirdly, the extracted sample features are taken as nodes, and the distance between features is taken as edge weight, and the graphs are constructed for the known samples and unknown samples. Finally, the meta-task-based graph learning method is used to learn the distribution rules of edge weights of graph nodes, and the label information of the known samples in the task is propagated to the unknown samples. The experimental results show that the proposed method outperforms the traditional small-sample fault diagnosis methods on both public datasets and aircraft engine casing datasets. Document identification code: A Chinese graphic classification number: TP315.69