Self‐supervised domain adaptation for cross‐domain fault diagnosis

Abstract

Unsupervised domain adaptation‐based fault diagnosis methods have been extensively studied due to their powerful knowledge transferability under different working conditions. Despite their encouraging performance, most of them cannot sufficiently account for the temporal dimension of the vibration signal, resulting in incomplete feature information used in the domain alignment procedure. To alleviate the limitation, we present a self‐supervised domain adaptation fault diagnosis network (SDAFDN), which considers two temporal dependencies to improve the transferability of the learned representations. Specifically, we first design a down‐sampling and interaction network that considers the temporal dependency among subsequences with low temporal resolution in feature space. Then, we combine domain adversarial learning with feature mapping to achieve domain alignment. Finally, we introduced a self‐supervised learning module, which considers the temporal dependency between the past and future temporal segments via classification tasks. Extensive experiments on public Paderborn University and PHM data sets demonstrate the superiority of the proposed SDAFDN and the effectiveness of considering temporal dependencies in domain alignment.

Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding

“…Some methods of selfsupervised learning only look at adding more data to find local and global connections. They don't look at the important connections between network structures and node attributes in the latent space [23], [24]. Several approaches, including twostep methods and graph auto-encoder techniques, are developed primarily to understand graph embedding, not to identify anomalous nodes, deviating from the main goal of anomaly detection [74].…”