FCNet: Deep Learning Architecture for Classification of Fatigue and Corrosion Acoustic Emission Signal

Joshi, Deepak and Yadao, Sudarshan and Bhange, Prasannata and Sunil Kumar, Pandu and Mankari, Kamal and Acharyya, Amit and et al, . (2022) FCNet: Deep Learning Architecture for Classification of Fatigue and Corrosion Acoustic Emission Signal. In: 20th IEEE International Interregional NEWCAS Conference, NEWCAS 2022, 19 June 2022 through 22 June 2022, Quebec City.

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Abstract

The recent advancement in semiconductor and computing technology has empowered the field of structural health monitoring in many ways. This work introduces a deep learning-based architecture, 'FCNet,' to predict acoustic emission signals arising from deformations like corrosion and fatigue crack. The suggested model uses a lightweight framework that takes advantage of the convolutional neural networks to demonstrate the implicit ability of feature identification, which removes the time-consuming stages of feature selection and extraction. The model's performance was proved using a dataset of 8566 corrosion and fatigue acoustic emission signals. To identify corrosion and fatigue acoustic emission signals, the model attained a 99.7 percent accuracy, demonstrating the efficacy of the suggested model for real-time reliability. The importance of this research for the industry is that it will provide a lethal approach for identifying metal deformation causes and, as a result, reducing accidents. © 2022 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amithttp://orcid.org/0000-0002-5636-0676
Item Type: Conference or Workshop Item (Paper)
Additional Information: This project is partially funded by Naval Research Board (NRB), Defence Research and Development Organization and partially by Science and Research Board (SERB), Government of India (GOI) under IMPRINT project. Authors would also like to acknowledge Dr K. Sridhar from Naval Materials Research Laboratory, GOI for his support.
Uncontrolled Keywords: Acoustic emission; Convolutional neural network; Corrosion; Fatigue
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 08 Oct 2022 06:41
Last Modified: 08 Oct 2022 06:41
URI: http://raiith.iith.ac.in/id/eprint/10853
Publisher URL: http://doi.org/10.1109/NEWCAS52662.2022.9842070
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