A multi-sensor fused incremental detection model for blade crack with cross-attention mechanism and Dempster-Shafer evidence theory

Deep learning-based blade crack detection models work on the premise of a fixed data distribution, while the influx of new dataset for faults under blade crack propagation often leads to a catastrophic forgetting problem. Meanwhile, it is difficult for a single sensor to reflect the health status of the blade comprehensively under the limitation of installation location and coverage. To solve the above problems, a multi-sensor fused incremental detection model (MFIDM) for blade cracks with the cross-attention mechanism and the Dempster-Shafer evidence theory (DST) is proposed. Firstly, vibration signals of centrifugal fans are collected by multiple accelerometers deployed at different locations. Then, a two-branch feature fusion method based on the cross-attention mechanism is proposed to overcome the class imbalance due to the replay incremental learning method. After that, the fused features are fed into a Softmax classifier to complete the initial classification of blade status. Finally, a modified DST based on the cross-correlation energy is adopted for multi-sensor decision fusion to obtain the final blade crack detection results. The effectiveness of the proposed method is verified by two incremental blade crack datasets, and MFIDM achieves the better performance compared with other related incremental detection methods.