Transfer-learning-based bearing fault diagnosis between different machines: A multi-level adaptation network based on layered decoding and attention mechanism

It has been a challenge to use the learned knowledge from collected labeled data of one machine to achieve the intelligent fault diagnosis of other machines. In this paper, a novel multi-level domain adaptation network based on layered decoding and attention mechanism (LDAM-MAN) is proposed for the transfer bearing fault diagnosis across machines using unlabeled data of practical machine. The architecture consists of shared and private feature extraction module, and layered decoding operation is adopted in the shared feature extraction module. Multi-level domain adaptation is developed to align the domain distribution. Attention mechanism is introduced to distribution adaptation to guarantee the features from source and target domains belong to same fault type. Six tasks of transfer fault diagnosis are designed using three different bearing datasets to validate the perfor-mance of proposed method, and the comparative experiment results show that the proposed method can achieve higher diagnosis accuracy and better transferability.

Automated summary

A novel multi-level domain adaptation network is proposed for transfer bearing fault diagnosis across machines, achieving higher diagnosis accuracy and better transferability through layered decoding and attention mechanism.