… learning method named Feature Space Metric-based Meta-learning Model (FSM3) for fault
diagnosis … effective metric-based meta-learning models for few-shot learning, ie, Matching …

… data samples for each fault category, making it … -shot learning framework for bearing fault
diagnosis based on model-agnostic meta-learning, which targets for training an effective fault …

… by MAML, the meta-learning strategy in MAML is … fault diagnosis approach based on
meta-learning is put forward in this paper including two procedures of feature extraction and fault …

… meta-learning-enabled method for the detection of fault in rolling bearings of electric
machines under varying working conditions with limited data. The fault diagnosis … meta-learning-…

… meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot
fault diagnosis … , metric-based meta-learning networks for mechanical fault diagnosis are …

… hierarchical recurrent meta-learning (DRHRML) is proposed for bearing fault diagnosis with
small … This approach contains data reconstruction and meta-learning stages. In the data …

… Meta-learning, also known as learning to learn, uses a small … -shot and cross-domain fault
diagnosis, and thus has become … investigates deep meta-learning in fault diagnosis from three …

… transfer learning to classify the faults in the target domain. The current studies of fault diagnosis
… a novel bearing fault diagnosis method, named meta-learning fault diagnosis (MLFD), to …

… our research on meta-learning-based mechanical fault diagnosis in the … learning with the
mechanisms of meta-learning might greatly improve the performance of cross-domain diagnosis…

… diagnosis methods. To address the few-shot fault diagnosis problem, a task-sequencing
meta-learning … First, the meta-learning model is trained over a series of learning tasks to obtain …