Design of intelligent fiber optic statistical mode sensors using novel features and artificial neural networks

Abstract

In this paper, novel statistical features namely first moment and second moment were proposed in the analysis of fiber optic statistical mode sensors to design consistent and sensitive sensors. These features used first time in literature. Experiments were conducted to measure force using statistical mode sensors and analyses were implemented using proposed statistical features. Good results were concluded and it was seen that these statistical features can be used in the design of statistical mode sensors. After that, Artificial Neural Networks (ANNs) with sensor fusion, intelligent sensor architecture was proposed to predict the force values measured by statistical mode sensors. It was seen that using the statistical features with sensor fusion provides better prediction of force values. Multilayer Perceptron (MLP) with different algorithms were used in ANN model. All of them can predict the force values with considerable errors. Statistical mode sensors can be calibrated and fault tolerance of the sensor can be decreased, hence more reliable sensors can be designed.