Pitting corrosion prediction based on electromechanical impedance and convolutional neural networks.

Corrosion induced thickness loss in metallic structures is a common and crucial problem in multiple industries. Therefore, it is important to accurately monitor the corrosion amount of the structure. Traditional corrosion monitoring methods are mainly based on electrochemical methods, and most of them are unable to quantify the corrosion amount. In our previous work, a new type of corrosion sensing mechanism based on the electromechanical impedance instrumented circular piezoelectric-metal transducer was proposed, in which the peak frequencies in the conductance signatures decrease linearly with the increase of the corrosion induced thickness loss. However, only the uniform corrosion with even metal thickness decrease was considered in the previous study. In this paper, the capability of the proposed sensing mechanism for the quantification and prediction of pitting corrosion was investigated using one-dimensional convolutional neural networks (1D CNN). Finite element modeling of the pitting corrosion was performed and the probability distribution of the corrosion pits was considered. In the experimental setup, corrosion pits were generated on the corrosion sensor using mechanical drilling. The 1D CNN was adopted to explore the regression relationship between the EMI signatures of the sensor and the mass loss induced by pitting corrosion. The results show that the proposed method has achieved high accuracy in the quantitative prediction of pitting corrosion. This paper lays the technical foundation for real-time and quantitative monitoring of pitting corrosion for metallic structures. [ABSTRACT FROM AUTHOR]