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Signal processing using artificial neural network for BOTDA sensor system.

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    Artificial neural networks (ANNs) process Brillouin optical time domain analyzer (BOTDA) signals for faster, more accurate distributed temperature sensing. This method bypasses traditional Brillouin frequency shift calculations, improving performance with varying signal linewidths.

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    Area of Science:

    • Optoelectronics
    • Fiber optic sensing
    • Artificial Intelligence

    Background:

    • Brillouin optical time domain analyzer (BOTDA) is a key technology for distributed sensing.
    • Traditional BOTDA signal processing relies on Brillouin frequency shift (BFS) determination, which can be complex and time-consuming.
    • Variations in Brillouin gain spectra (BGS) linewidths can impact sensing accuracy, especially over long distances.

    Purpose of the Study:

    • To demonstrate the efficacy of artificial neural networks (ANNs) in processing BOTDA signals for direct temperature extraction.
    • To develop an ANN model capable of handling linewidth variations in BGS for long-distance sensing applications.
    • To compare the performance of ANN-based temperature extraction with conventional methods like Lorentzian curve fitting and cross-correlation.

    Main Methods:

    • Experimental setup utilizing BOTDA to acquire sensing signals.
    • Development and training of an ANN using ideal Brillouin gain spectra (BGS) with varying linewidths.
    • Comparative analysis of ANN performance against Lorentzian curve fitting and cross-correlation methods.

    Main Results:

    • ANN directly extracts temperature information from BGS, eliminating the need for BFS calculation.
    • The ANN model demonstrates high accuracy and tolerance to measurement errors, particularly with large frequency scanning steps.
    • ANN-based temperature extraction is significantly faster than traditional methods, enabling reduced measurement times without compromising accuracy.

    Conclusions:

    • ANN offers a robust and efficient alternative for processing BOTDA signals for distributed temperature sensing.
    • The proposed ANN approach is particularly advantageous for long-distance sensing and applications requiring rapid measurements.
    • ANN processing enhances BOTDA system performance by improving speed and accuracy, especially when large frequency scanning steps are employed.