Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection

Streaming data of machines is continuously collected in practical applications, which produces new fault information with respect to the health change. Therefore, a lifelong-learning intelligent diagnosis model is desired for new fault type recognition based on the streaming data. However, existing research in intelligent fault diagnosis always treats new fault type detection and class incremental learning as two independent problems, which reduces their practicality in industrial applications. To tackle this limitation, a graph continual learning network is constructed for incremental intelligent diagnosis of new faults. The method integrates the advantages of both new fault type detection and class incremental learning. In the method, a graph convolutional network (GCN) based model is formulated for detecting new classes to prejudge whether the DL model needs to be updated. Once any new class is detected, class incremental learning is started automatically to update the DL model without leading to catastrophic forgetting. The proposed method is applied to a pump fault diagnosis case with incremental fault types. Results show that the proposed method offers an effective solution for online intelligent fault diagnosis with satisfactory classification performance