An optical fibre ethanol concentration sensor utilizing Fourier transform signal processing analysis and artificial neural network pattern recognition

An optical fibre sensor, which is capable of detecting varying percentages of ethanol in water, is reported. In order to maximize sensitivity, a U-bend configuration was used for the sensor where the cladding was removed and the core exposed directly to the fluid under test. The sensor was interrogated using optical time domain reflectometry (OTDR). OTDR is chosen as it is a recognized technique for the interrogation of distributed multipoint sensors and it is intended to extend this work to multiple sensors on a single fibre in the future. In this investigation the sensor was exposed to 12.5, 25 and 50% ethanol and distilled water. The signal processing technique has been designed to optimize the neural network adopted in the existing sensor system. In this investigation a discrete Fourier transform, using a fast Fourier transform algorithm, is chosen and its application leads to an improvement in efficiency of the neural network, i.e. reducing the number of input and hidden layer nodes required by the artificial neural network. Using a Stuttgart neural network simulator, a feed-forward three-layer neural network was constructed with the aim of successfully classifying the sensor test conditions based on the frequency domain response of the sensor.