Neural-Network-Assisted Compensation for Enhanced High-Temperature Pressure Measurement Accuracy Using a Silica-Diaphragm Fiber-Optic Fabry–Perot Sensor

Accurate pressure measurement under high-temperature conditions is challenging for silica-diaphragm-based fiber-optic Fabry–Perot (F-P) sensors because temperature causes both optical cavity length (OCL) baseline drift and pressure-sensitivity variation. In this work, a structurally simple and readily fabricated silica-diaphragm-based fiber-optic F-P pressure sensor was developed, and a neural-network-assisted compensation strategy was proposed to suppress the residual errors of conventional analytical compensation. A temperature-dependent response model was established to describe OCL drift and sensitivity variation. The OCL was demodulated from reflection spectra using an FFT-assisted dual-peak and MMSE refinement method, and static pressure measurements were performed over 25–400 °C and 0–2.4 MPa. Based on the experimentally verified response characteristics, a fitting-based compensation method considering both OCL drift and sensitivity variation was first implemented. A lightweight neural network was then constructed using the OCL variation, ΔOCL, and ambient temperature as physically meaningful input features. Compared with fixed-sensitivity compensation and drift-and-sensitivity fitting compensation, whose maximum full-scale errors were 7.10% F.S. and 2.74% F.S., respectively, the proposed method reduced the maximum error to 0.90% F.S. with an RMSE of 0.0045 MPa. Additional validation at the independent intermediate temperatures of 150, 250, and 350 °C further confirmed the generalization capability of the proposed NNC model between calibrated temperature gradients, achieving an overall RMSE of 0.0055 MPa and a maximum full-scale error below 0.77% F.S. The proposed approach provides a high-accuracy and practical solution for high-temperature pressure monitoring using simple fabricated silica-diaphragm F-P sensors.