Research Area: Machine Learning
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.
Keywords:
Intelligent fault diagnosis
Prior knowledge embedding
Few-shot learning
Meta-transfer learning
Variable operating conditions
Author(s) Name: Zihao Lei, Ping Zhang, Yuejian Chen, Guangrui Wen
Journal name: Elsevier Mechanical Systems and Signal Processing
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.ymssp.2023.110491
Volume Information: Volume 200
