A Multitask Crisscross Network for One-Shot Bearing Multiattribute Fault Diagnosis

The scarcity of samples presents a significant challenge in data-driven fault diagnosis, particularly for bearing malfunctions where swift shutdowns are imperative to avert accidents. This operational constraint makes it difficult to obtain bearing fault data, resulting in catastrophic performance on the test set, particularly when only a single sample is available for each fault. This article proposes a multitask crisscross network (MTCCN) for one-shot fault diagnosis based on transfer learning theory. The semantic information of faults is treated as attributes, serving as knowledge to be transferred. A multitask learning approach is used to predict multiple attributes, which provides more valuable fault information. The horizontal structure of MTCCN is a task-sharing network, where global features are extracted for task-specific networks to predict attributes. The vertical structure consists of an attribute classifier chain (ACC), and the correlation between attributes is modeled by a directed acyclic graph (DAG) for the attribute prediction tasks. An information map integrates multiple heterogeneous sources of information to improve prediction accuracy. Finally, extensive experiments were conducted on the different datasets, and the transfer effect was quantified, which demonstrated the efficiency and robustness of MTCCN.