Multi-scale and multi-view fusion-based state space model for bridge health monitoring sensor fault diagnosis

As bridge infrastructure ages and load increases, Bridge Health Monitoring Systems (BHMS) have become increasingly important. Sensors play a crucial role in BHMS, but sensor failures may result in inaccurate data, thereby reducing the reliability of the monitoring system. Due to the interference from the vehicles on the bridge, current bridge sensor fault detection algorithms cannot detect the faults of sensors accurately. To tackle this challenge, we combine the advanced State Space Model (SSM, known as Mamba) with Transformer and propose a novel bridge sensor fault diagnosis method MSMV-MT. Firstly, MSMV-MT samples the sensor sequences at different scales and adopts Past Decomposable Mixing (PDM) for multi-scale fusion to capture at different-scaled features. MT block is constructed by replacing the Multi-head Attention with bidirectional Mamba in Transformer. We introduced a Full Sequence Channel Attention (FSCA) mechanism in the MT block to weight the attention of sequences of different channels and perform multi-view fusion with Self-Attention, thereby enhancing the accuracy and robustness of fault diagnosis. We constructed a simulation dataset with collected bridge sensor data and conducted experimental analysis. The experimental results show that the MSMV-MT outperforms all compared algorithms and achieves the highest accuracy of 92.7% on the bridge sensor fault diagnosis dataset. Comprehensive comparative experiments and ablation analysis have demonstrated the effectiveness of the proposed scheme.