Novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in adaptive threshold network

Abstract

Fault diagnosis of rotating machinery based on deep learning requires many identical labeled data to achieve satisfactory results. However, in engineering scenarios, few typical fault data have been manually marked, resulting in a poor on-site diagnosis consequence. Thus, training an efficient cross-domain diagnosis model under limited data is extremely challenging. This paper proposes a novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in Adaptive threshold network (ATN-MAML). The threshold acquisition module is built to determine the signal's irrelevant information threshold adaptively. The designed threshold function is embedded in the network to filter out invalid information effectively as a non-linear layer. Then, MAML's training strategy is optimized to prevent overfitting. Finally, many cross-domain comparison experiments are conducted to verify the effectiveness of the proposed method on two datasets. The results show the model can maintain high accuracy even when the source and target domain data are from different devices.