Transformer fusion of asynchronous PZT guided-wave and FBG strain data yields HI MAE/RMSE <0.1 and localization MAE/RMSE <0.0465/0.1571, beating single-sensor and SOTA DNN baselines by ~60% on ReMAP composite fatigue panels.
Multimodal deep neural network- based sensor data anomaly diagnosis method for structural health monitoring.Buildings 2023;13(8):1976
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Transformer-based Multisensor Data Fusion of Ultrasonic Guided Wave and FBG-based Strain Measurements for Multitask Aerospace Structural Health Monitoring
Transformer fusion of asynchronous PZT guided-wave and FBG strain data yields HI MAE/RMSE <0.1 and localization MAE/RMSE <0.0465/0.1571, beating single-sensor and SOTA DNN baselines by ~60% on ReMAP composite fatigue panels.