Seawater Dual Parametric Fiber Optic Temperature and Pressure Sensors Demodulated by Machine Learning Method

Abstract

By employing polydimethylsiloxane (PDMS) characterized by elevated thermo-optical and elastic-optical coefficients for encapsulating an optical microfiber coupler integrated with a sagnac loop (OMCSL) structure, which exhibits large abrupt field characteristics, a fiber optic sensor capable of simultaneously measuring seawater temperature and pressure can be created. Nevertheless, the utilization of the traditional sensitivity matrix method (SMM) for demodulating the sensor led to unstable and considerably erroneous demodulation results. To enhance the accuracy of the demodulation process, this paper investigates and employs a machine learning method (MLM) for sensor demodulation. This paper centers on the investigation and application of MLM for sensor demodulation. The experimental results exhibit a significant decrease in demodulation error attained via MLM when contrasted with the traditional SMM.