Stress and defect detection of specimens based on tag array sensing technology

To meet the quality and accuracy requirements of structural health detection, this study is based on the Radio Frequency Identification tag array sensing technology as the core, and designs a method for metal specimen defect detection and material bending stress assessment. The experiment shows that the root mean square error of the designed fixed frequency analysis startup power algorithm and the error result of the Rsquared index in the ultra-high frequency band are at the minimum level, which is suitable for the working frequency band of metal specimen defect detection. At the same time, the accuracy and recall index values of this algorithm are relatively high, located in the range of 84.41%-90.27% and 78.17%-90.26%, respectively. The application of tag array sensing defect detection technology in the evaluation of metal defect specimens and deflection bending stress is effective, and there are significant differences in the distribution of characteristic values and power levels between healthy and defective areas, indicating a good discrimination effect. This study enriches the theoretical foundation and application practice of tag array sensing technology in the field of structural non-destructive health monitoring, facilitates comprehensive stress monitoring of structures, and improves the robustness of structural monitoring schemes.