LSTM-based Node-Gated Graph Neural Network for Cross-condition Few-shot Bearing Fault Diagnosis

In practical scenarios, the working conditions of bearings change with the variation of work tasks, making it extremely challenging to collect a large number of fault samples for each working condition. This difficulty hinders existing fault diagnosis methods from achieving satisfactory diagnostic results. To address this issue, this paper proposes a novel long short-term memory (LSTM)-based node-gated graph neural network (LNGGN) for cross-condition few-shot bearing fault diagnosis. LNGGN is primarily composed of four modules: the graph initialization module, the LSTM-based node-gated update module (LNGUM), the mean-based node attention module (MNAM), and the edge update module (EUM). The graph initialization module is responsible for converting raw data into graph data. LNGUM is proposed to implement gated updates for node features, it effectively retains the essential information of nodes, alleviating the problem that node features become indistinguishable with the increasing number of updates. Moreover, LNGUM facilitates adaptive filtering of aggregated information, thereby reducing the impact of potential data noise on nodes. MNAM is designed to emphasize the more expressive feature dimensions within nodes for improved similarity computation. Within the EUM, two metric networks are employed to calculate the similarity and dissimilarity between nodes, which are then used to update the edge features. Experimental evaluations are conducted on two bearing datasets to validate the effectiveness of LNGGN. The results demonstrate that LNGGN outperforms baseline methods significantly in cross-condition few-shot bearing fault diagnosis tasks.