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Ambient temperature variations frequently induce measurement errors in fiber-optic Fabry-Perot strain sensors. To effectively compensate for temperature effects, this study systematically investigates a single-mode fiber-hollow-core fiber-single-mode fiber (SMF-HCF-SMF) Fabry-Perot fiber strain sensor. Experimental datasets were collected under varying temperature and strain conditions, with Kalman filtering employed for noise suppression and data preprocessing. The processed data were subsequently fed into an optimized PSO-BP neural network, where temperature values and spectral peak shift measurements from the sensor served as direct inputs to establish a temperature-compensated neural network model. The algorithm incorporates adaptive adjustment mechanisms for inertial weights and learning factors, significantly enhancing global search capability and local convergence accuracy.Experimental results demonstrate that the optimized PSO-BP algorithm achieves a mean absolute percentage error (MAPE) of 1.2% across the sensor's operational temperature range, with R2 values consistently exceeding 0.995 under diverse thermal conditions. Comparative analyses reveal that the optimized PSO-BP model improves MAPE by 57.14%, 45.45%, 73.91%, and 53.85% relative to conventional BP, PSO-BP, RF, and GA-BP models, respectively. Corresponding reductions in root mean square error (RMSE) reach 68.11%, 52.42%, 72.94%, and 63.13%. These metrics substantiate the algorithm's superior strain prediction performance and effective temperature compensation capability. The proposed methodology provides a novel technical pathway for developing cost-effective, high-precision sensing systems with robust thermal stability.
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Keywords:
- optical fiber sensor /
- optimized PSO-BP neural network /
- temperature compensation /
- Fabry-Perot interference
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