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

Shenglin Wei; Zongbao Liang

doi:10.1117/12.3051332

18 November 2024 Multi-scale and multi-view fusion-based state space model for bridge health monitoring sensor fault diagnosis

Shenglin Wei, Zongbao Liang

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033U (2024) https://doi.org/10.1117/12.3051332
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shenglin Wei and Zongbao Liang "Multi-scale and multi-view fusion-based state space model for bridge health monitoring sensor fault diagnosis", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033U (18 November 2024); https://doi.org/10.1117/12.3051332

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KEYWORDS

Sensors

Bridges

Transformers

Deep learning

Performance modeling

Ablation

Feature extraction

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