Interpreting complex data from a three-sensor multipoint optical fibre ethanol concentration sensor system using artificial neural network pattern recognition

A three-sensor element multipoint optical fibre sensor system capable of detecting varying ethanol concentrations in water for use in industrial process water systems is reported. The sensor system utilizes a U-bend configuration for each sensor element in order to maximize the sensitivity of each of the sensing regions along the optical fibre cable. The sensor system is interrogated using a technique known as optical time domain reflectometry, as this method is capable of detecting attenuation over distance. Analysis of the data arising from the sensor system is performed using artificial neural network pattern recognition techniques, coupled with Fourier-transform-based signal processing. The signal processing techniques are applied to the obtained sensor system data, prior to the artificial neural network analysis, with the aim of reducing the computational resources required by the implemented artificial neural network.

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