Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions

Abstract

In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous development thanks to its powerful feature representation ability. However, scarcity of high-quality data, especially samples under severe fault states, and variable operating conditions have limited the industrial application of intelligent fault diagnosis. To alleviate this predicament, a novel prior knowledge-embedded meta-transfer learning (PKEMTL) is proposed for few-shot fault diagnosis with limited training data and scarce test data. The method focuses on the problem of few-shot fault diagnosis under variable operating conditions to improve adaptability. Different from traditional models, the PKEMTL employs a metric-based meta-learning framework and embeds prior knowledge to enable cross-task learning under variable operating conditions. Specifically, order tracking is firstly introduced as preliminary prior information for data augmentation, and then the augmented data are divided into a series of meta-tasks. Secondly, the meta-tasks are performed by lightweight multiscale feature encoding to obtain high-level feature representations. Next, the meta-learning module based on diagnostic knowledge embedding guides the model to acquire meta-knowledge of speed generalization by constructing the self-supervised task to embed additional prior knowledge into the meta-training process. The generalization performance of the model is further improved by adaptive information fusion learning as a comprehensive decision-making module. Two case studies under variable operating conditions are implemented to validate the effectiveness and superiority of the proposed few-shot fault diagnosis method.