Optical Vernier effect-based demodulation system for fiber-optic sensors: an AWG and a neural network-based...

Area of Science:

• Optoelectronics
• Sensor Technology
• Artificial Intelligence

Background:

• Accurate demodulation of fiber-optic sensors is critical for engineering applications.
• Conventional methods struggle with generalization for complex sensor data.
• Arrayed waveguide gratings (AWGs) offer potential for spectral encoding.

Purpose of the Study:

• To develop a novel demodulation method for fiber-optic sensors using neural networks and AWGs.
• To achieve absolute wavelength interrogation without extensive datasets.
• To enhance sensor sensitivity and efficiency in monitoring and control.

Main Methods:

• Integration of neural networks with arrayed waveguide gratings (AWGs).
• Utilizing the Vernier effect and a novel Fabry-Pérot (FP) strain sensor.
• Employing neural networks to model nonlinear relationships between light intensity and wavelength.

Main Results:

• Demonstrated low energy consumption and high efficiency in demodulation.
• Achieved accurate absolute wavelength interrogation.
• Showcased enhanced sensor sensitivity through the proposed method.

Conclusions:

• The proposed neural network and AWG-based demodulation method is effective for fiber-optic sensors.
• This approach overcomes limitations of conventional demodulation techniques.
• It offers a promising solution for real-world monitoring and control applications.