Convolutional Neural Networks applied in the monitoring of metallic parts

This work presents an intelligent structural monitoring system. It consists of two steps: the first one is a non- destructive test using electromechanical impedance and the second one, an impedance curve is classified by a deep learning algorithm, Convolutional Neural networks. The experiments were...

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Bibliographic Details
Main Authors: Almeida, J. H. L, Lopes, L. A. R, Silva, M. A. B, Amaral, J. L. M.
Format: Conference Proceeding
Language:English
Subjects:
Classifiers
Deep learning
electromechanical impedance method
Gas turbine blades
metallic parts
Structure health monitoring
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Summary:This work presents an intelligent structural monitoring system. It consists of two steps: the first one is a non- destructive test using electromechanical impedance and the second one, an impedance curve is classified by a deep learning algorithm, Convolutional Neural networks. The experiments were performed using two different ways of handling on the input vector: keep it one-dimensional and convert it into a two-dimensional array. The electromechanical impedance test was performed through using PZT transducers coupled with 1020 carbon steel plates, which simulate the turbine vane with different damages. This study reveals the interesting concept of Convolutional Neural Networks in Structural Integrity Monitoring, converting the extracted information into an image recognition task.
ISSN:2161-4407
DOI:10.1109/IJCNN.2018.8489774