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Shape function method based on truncated singular value decomposition regularization for hull longitudinal bending moment identification
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DOI：10.1016/j.measurement.2024.114703.png)
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Abstract
En 中文
The longitudinal bending moment plays a crucial role in hull structural health monitoring. To enhance its identification accuracy, a refined method based on shape function method was developed. It incorporated truncated singular value decomposition (TSVD) regularization within shape function method of moving least square fitting(SFM_MLSF) and was combined with the influence coefficient matrix method (ICM). Numerical and experimental studies were conducted for validation. Numerical results showed that ICM-based SFM_MLSF significantly improved the identification accuracy. It achieved a reduction of over 40% in both root mean square error (RMSE) and mean absolute error (MAE) compared to ICM at different sampling frequencies. SFM_TSVD outperformed SFM_MLSF, with a maximum reduction of 57% in RMSE and MAE for various neighborhood lengths and base function scales. Experimental investigations corroborated the viewpoints that SFM_TSVD manifesting superior performance in comparison to SFM_MLSF. It demonstrates the potential of SFM_TSVD for advancing load identification within structural health monitoring.
Keywords:
Ship structure health monitoring
Longitudinal bending moment identification
Shape function method
TSVD regularization
Influence coefficient matrix method.