Adaptive neighborhood-perceived contrastive network for early stage fault diagnosis of rolling bearing with limited labeled data

Early stage fault diagnosis is vital for rotating machinery to reduce accidents and increase stability. It is a challenging task since weak fault pattern is presented in signals and limited labeled data escalate the hardness of learning salient fault features. To address the gap, we propose a novel adaptive neighborhood-perceived contrastive network (ANPCN). It enhances features by adaptively aggregating representations of the samples that have high anchor-based correlations, and restraining the connections among samples that have low correlations. Specifically, based on mapped time–frequency representations, a graph with dynamic edges is constructed by measuring designed anchor-based similarity based on limited labels. In ANPCN, a graph learning path learns fault features using a graph neural network (GNN) that can perceive neighborhood from the graph. A contrastive learning path learns prototype features by integrating data views and the edge topology of the graph, utilizing a key parameter α to combine them. The prototype features serve as a supplement to the limited labels, guiding the learning of the GNN and updating dynamic edges. Fault features are enhanced by alternatively optimizing the two paths. The effectiveness of the proposed approach is validated on a private bearing testbed and two public benchmarks, reaching a diagnosis accuracy of 96.63% on our dataset with only 5% labeled data.