Water and Ice Detection in Marine Icing by Capacitive Sensor Array and the Artificial Neural Network Model

Elzaidi, Abdulrazak; Mašek, Vlastimil; Bruneau, Stephen

doi:10.1109/iemcon.2019.8936232

Cited by 3 publications

(3 citation statements)

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“…Applying the trained decision tree model to the test data results in the ice accretion estimate depicted in Figure 8 and the water layer estimate depicted in Figure 9. Both profiles show a significant improvement over the earlier results reported in [12] and a corresponding results to the ANN approach in terms of the ice accretion [13]. The Precision, Recall, F-Measure and Accuracy are commonly used for evaluating classification methods in Machine learning [26].…”

Section: Resultsmentioning

confidence: 91%

“…Our team developed a multi-dimensional least-squares-method [ 12 ] to map the measured data to the measurands. The least-squares method was later substituted by a more robust and more accurate technique based on artificial neural networks method [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…Previously we processed the array signals by a multidimensional least squares method [ 12 ] and in another approach by artificial neural networks [ 13 ]. In this work, we applied a machine learning strategy centered around a decision tree method.…”

Section: Introductionmentioning

confidence: 99%

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Marine Icing Sensor with Phase Discrimination

Elzaidi

Mašek

Bruneau

2021

Sensors

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In this paper, a novel approach is presented to the measurement of marine icing phenomena under the presence of a two-phase condition. We have developed a sensor consisting of an electrostatic array and a signal processing based on a decision tree method. A three-element electrostatic array is employed to derive signals having linearly decoupled characteristics from which two key parameters, ice and water accretion layer dimension, can be determined for the purpose of environmental monitoring. The quantified characteristics revealed a correlation with the ice layer thickness in spite of the strong influence from the top water phase layer. The decision tree model established a relationship between the signal characteristics and the two accretion thickness parameters of water and ice layer. Through experimental verification, it has been observed that our sensor array in combination with the decision tree model based signal processing provides a simple practical solution to the challenging field of a two phase composition measurement such as in the marine icing considered in this study.

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Section: Resultsmentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%