A modal interference-based Fiber Optic Sensor for dual parameter measurement using an Artificial Neural Network

Abstract

Abstract Singular sensors for multiple parameter measurements allows greater sensing fidelity at reduced logistical requirements. Distributed Fiber Optic Sensors provides the aforementioned advantage but also has significant drawbacks in terms of signal processing which includes noise rejection and compensation for the dynamics of the environment. Furthermore, Fiber Optic Sensors, are usually serially multiplexed, causing an overlapping of signals at the output and fault intolerance. In this article, a fiber modal interferometer based on the interference of linearly polarized modes in birefringent optical fiber for dual parameter measurement of strain and temperature has been proposed. The beat signal produced due to the interference of two specifically excited lower order modes (LP01 and LP11) was detected using a photodetector. A signal processing method based on Fast Fourier Transform was used to develop the harmonics and monitor the beat frequencies corresponding to the location of the physical parameters. The amplitudes of the beat frequencies were used by a pre-trained Artificial Neural Network to ascertain the measurand information(s). Results for applied strain in the range of 300–2500 μstrain and for temperature values between 20 and 60 °C are presented.