Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants

Malik Al Abed Allah; Afaque Shams; Ihsan Ul Haq Toor; Naveed Iqbal

doi:10.1007/978-3-031-64362-0_28

Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants

Malik Al Abed Allah^*, Afaque Shams, Ihsan Ul Haq Toor, Naveed Iqbal

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The detection of corrosion and cracks in nuclear power plants is a critical task that requires accurate and efficient monitoring systems. Traditional inspection methods can be time-consuming and may not be able to detect defects in hard-to-reach areas. Machine learning and deep learning have shown promising results as replacements for conventional ways of detecting corrosion and cracks in nuclear power reactors in recent years. This paper compares the latest research on machine/deep learning techniques for corrosion and crack detection in nuclear power plants. It includes an overview of the different machine/deep learning algorithms that have been applied in this field. Furthermore, this paper also investigates the effect of different input features and transfer learning techniques on the accuracy of corrosion and crack detection models. Finally, a systematic review of publicly available datasets for corrosion and crack detection in nuclear power plants is presented.

Original language	English
Title of host publication	Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE
Editors	Afaque Shams, Khaled Al-Athel, Iztok Tiselj, Andreas Pautz, Tomasz Kwiatkowski
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	279-287
Number of pages	9
ISBN (Print)	9783031643613
DOIs	https://doi.org/10.1007/978-3-031-64362-0_28
State	Published - 2024
Event	Saudi International Conference on Nuclear Power Engineering, SCOPE 2023 - Dhahran, Saudi Arabia Duration: 13 Nov 2023 → 15 Nov 2023

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	Saudi International Conference on Nuclear Power Engineering, SCOPE 2023
Country/Territory	Saudi Arabia
City	Dhahran
Period	13/11/23 → 15/11/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

corrosion
cracks
deep learning
machine learning
nuclear power plants

ASJC Scopus subject areas

Automotive Engineering
Aerospace Engineering
Mechanical Engineering
Fluid Flow and Transfer Processes

Access to Document

10.1007/978-3-031-64362-0_28

Cite this

Allah, M. A. A., Shams, A., Toor, I. U. H., & Iqbal, N. (2024). Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants. In A. Shams, K. Al-Athel, I. Tiselj, A. Pautz, & T. Kwiatkowski (Eds.), Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE (pp. 279-287). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-64362-0_28

Allah, Malik Al Abed ; Shams, Afaque ; Toor, Ihsan Ul Haq et al. / Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants. Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE. editor / Afaque Shams ; Khaled Al-Athel ; Iztok Tiselj ; Andreas Pautz ; Tomasz Kwiatkowski. Springer Science and Business Media Deutschland GmbH, 2024. pp. 279-287 (Lecture Notes in Mechanical Engineering).

@inproceedings{d36f98e0d4c843e3bdc8e14e8ed82712,

title = "Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants",

abstract = "The detection of corrosion and cracks in nuclear power plants is a critical task that requires accurate and efficient monitoring systems. Traditional inspection methods can be time-consuming and may not be able to detect defects in hard-to-reach areas. Machine learning and deep learning have shown promising results as replacements for conventional ways of detecting corrosion and cracks in nuclear power reactors in recent years. This paper compares the latest research on machine/deep learning techniques for corrosion and crack detection in nuclear power plants. It includes an overview of the different machine/deep learning algorithms that have been applied in this field. Furthermore, this paper also investigates the effect of different input features and transfer learning techniques on the accuracy of corrosion and crack detection models. Finally, a systematic review of publicly available datasets for corrosion and crack detection in nuclear power plants is presented.",

keywords = "corrosion, cracks, deep learning, machine learning, nuclear power plants",

author = "Allah, {Malik Al Abed} and Afaque Shams and Toor, {Ihsan Ul Haq} and Naveed Iqbal",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; Saudi International Conference on Nuclear Power Engineering, SCOPE 2023 ; Conference date: 13-11-2023 Through 15-11-2023",

year = "2024",

doi = "10.1007/978-3-031-64362-0_28",

language = "English",

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series = "Lecture Notes in Mechanical Engineering",

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pages = "279--287",

editor = "Afaque Shams and Khaled Al-Athel and Iztok Tiselj and Andreas Pautz and Tomasz Kwiatkowski",

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Allah, MAA, Shams, A , Toor, IUH & Iqbal, N 2024, Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants. in A Shams, K Al-Athel, I Tiselj, A Pautz & T Kwiatkowski (eds), Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 279-287, Saudi International Conference on Nuclear Power Engineering, SCOPE 2023, Dhahran, Saudi Arabia, 13/11/23. https://doi.org/10.1007/978-3-031-64362-0_28

Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants. / Allah, Malik Al Abed; Shams, Afaque ; Toor, Ihsan Ul Haq et al.
Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE. ed. / Afaque Shams; Khaled Al-Athel; Iztok Tiselj; Andreas Pautz; Tomasz Kwiatkowski. Springer Science and Business Media Deutschland GmbH, 2024. p. 279-287 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Allah, Malik Al Abed

AU - Shams, Afaque

AU - Toor, Ihsan Ul Haq

AU - Iqbal, Naveed

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - The detection of corrosion and cracks in nuclear power plants is a critical task that requires accurate and efficient monitoring systems. Traditional inspection methods can be time-consuming and may not be able to detect defects in hard-to-reach areas. Machine learning and deep learning have shown promising results as replacements for conventional ways of detecting corrosion and cracks in nuclear power reactors in recent years. This paper compares the latest research on machine/deep learning techniques for corrosion and crack detection in nuclear power plants. It includes an overview of the different machine/deep learning algorithms that have been applied in this field. Furthermore, this paper also investigates the effect of different input features and transfer learning techniques on the accuracy of corrosion and crack detection models. Finally, a systematic review of publicly available datasets for corrosion and crack detection in nuclear power plants is presented.

AB - The detection of corrosion and cracks in nuclear power plants is a critical task that requires accurate and efficient monitoring systems. Traditional inspection methods can be time-consuming and may not be able to detect defects in hard-to-reach areas. Machine learning and deep learning have shown promising results as replacements for conventional ways of detecting corrosion and cracks in nuclear power reactors in recent years. This paper compares the latest research on machine/deep learning techniques for corrosion and crack detection in nuclear power plants. It includes an overview of the different machine/deep learning algorithms that have been applied in this field. Furthermore, this paper also investigates the effect of different input features and transfer learning techniques on the accuracy of corrosion and crack detection models. Finally, a systematic review of publicly available datasets for corrosion and crack detection in nuclear power plants is presented.

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BT - Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE

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ER -

Allah MAA, Shams A , Toor IUH , Iqbal N. Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants. In Shams A, Al-Athel K, Tiselj I, Pautz A, Kwiatkowski T, editors, Challenges and Recent Advancements in Nuclear Energy Systems - Proceedings of Saudi International Conference on Nuclear Power Engineering SCOPE. Springer Science and Business Media Deutschland GmbH. 2024. p. 279-287. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-031-64362-0_28

Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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