… meta-learning (DRHRML) is proposed for bearing fault diagnosis with small samples
under different working conditions. This approach contains data reconstruction and meta-learning …

… and working well under complex working conditions remains a challenge. In this paper, a
novel meta-learning fault diagnosis method (MLFD) based on model-agnostic meta-learning is …

… However, in the real world, different working conditions may contain different levels of noise,
… complex working conditions, in this article, we propose a model-agnostic meta-learning (…

… In Section II, we introduce the background knowledge of metalearning and formally
analyze the few-shot fault diagnosis problem under varying working conditions. The proposed …

… meta-learning framework is developed for diagnosing new faults from unseen working
conditions … It is noteworthy that the working condition for collecting fault signals in NNovel differs …

… of bearing under complex working conditions. Gradient-based meta-learning with simple
structure … , multiple state parameters and variable working conditions, this paper proposed EML …

… meta-learning and variational autoencoder (DML-VAE) is applied for coupling fault diagnosis
of rolling bearing under variable working conditions. … and other working condition samples …

… working conditions and limited data, we develop a novel cross-domain diagnosis method
based on model agnostic meta-learning (… Then, the construction strategy of meta-task in cross-…

… However, the lack of labeled samples and complex working conditions … Meta-Learning
(IFML) is explored for fault diagnosis with few-shot problems under different working conditions. …

… We assume the self-adaptive learner contains a meta-learner that takes as input the perfor…
using a meta-learner over Tmeta to discover the conditions under which a learning algorithm …