Research on sensitivity characteristics monitoring and teaching system of resistance strain sensor based on digital circuit signal conditioning

In response to the problem of low digital signal conditioning accuracy in traditional resistance strain sensors, this study constructs a sensitivity monitoring and teaching system that integrates digital circuit technology. Based on the SET-N experimental instrument, single arm, half bridge, and full bridge circuit models were built on the Keda Aorui Cloud platform, and multiple experiments were conducted to verify the effect of digital conditioning on improving sensor sensitivity. The results show that after digital filtering and polynomial correction, the sensitivity of the full bridge circuit reaches 1.248V/N, which is 4.03 times higher than that of a single arm, and the nonlinear error is reduced from 0.8% to 0.27%. The system integrates simulation and real-time processing functions, realizing the integration of sensor principle verification, signal conditioning, and experimental teaching, providing a new solution for the digital reform of engineering experimental teaching.