Single-ended self-calibration high-accuracy Raman distributed temperature sensing based on multi-core fiber.

Area of Science:

• Fiber optic sensing
• Temperature measurement
• Signal processing

Background:

• Raman distributed temperature sensing (RDTS) measures temperature using Raman scattering light intensities.
• Anti-Stokes only RDTS minimizes errors from wavelength-dependent loss and dispersion.
• Conventional methods for eliminating intensity variations often require inconvenient double-ended detection schemes.

Purpose of the Study:

• To develop a more practical and accurate single-ended RDTS system.
• To eliminate temperature-independent intensity variations in RDTS.
• To improve the temperature accuracy and reduce uncertainty in distributed sensing.

Main Methods:

• Utilizing a multi-core fiber (MCF) to create a single-ended loop structure.
• Connecting two cores of the MCF using a fan-in/fan-out device.
• Measuring backscattered anti-Stokes light in two cores for self-calibration.
• Employing a one-dimensional denoising convolutional neural network for enhanced temperature uncertainty reduction.

Main Results:

• Successfully demonstrated a self-calibrated, single-ended RDTS system using MCF.
• Achieved improved temperature accuracy by averaging measurements from two cores.
• Reduced temperature uncertainty using a convolutional neural network.
• Obtained a maximum temperature uncertainty of 1.4 °C over a 10 km MCF with 3 m spatial resolution.

Conclusions:

• The proposed MCF-based RDTS system offers a practical solution for accurate, single-ended temperature sensing.
• The self-calibration and averaging techniques effectively mitigate intensity variations and improve accuracy.
• The integration of deep learning further enhances temperature measurement precision in distributed sensing applications.