Enhanced Defect Detection in NDE Using Pixel Level Data Fusion

Subrata Mukherjee; Lalita Udpa; Yiming Deng

doi:10.23919/ACES57841.2023.10114697

Enhanced Defect Detection in NDE Using Pixel Level Data Fusion

Subrata Mukherjee, Lalita Udpa, Yiming Deng

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

Abstract

Pipeline systems in service continue to degrade with passage of time due to usage. Magnetic flux leakage (MFL) and Eddy Current (EC) based Non destructive evaluation (NDE) methods are most common for inline inspection (ILI) of the metallic pipelines. Many times, a single measurement technique fails to reveal all the characteristics of the damage due to resolution constraints. This paper presents a novel pixel based fusion of both MFL and EC inspection that can detect real defects like cracks and slits of sub millimeter dimension. An automated data based screening rule is developed to judge whether fusion is needed. Then a transform domain based fusion method called complementary sparse representation (CSR) along with post processing steps is developed for fusion of the source images. The efficacy of the fusion algorithm is studied on both the MFL and EC data generated by simulation means.

Original language	English
Title of host publication	2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781733509633
DOIs	https://doi.org/10.23919/ACES57841.2023.10114697
State	Published - 2023
Externally published	Yes
Event	2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023 - Monterey, United States Duration: Mar 26 2023 → Mar 30 2023

Publication series

Name	2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

Conference

Conference	2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023
Country/Territory	United States
City	Monterey
Period	03/26/23 → 03/30/23

Funding

This work is partially supported by the US Department of Transportation Grant: Knowledge-guided Automation for Integrity Management of Aging Pipelines (KAI-MAP) for Hydrogen Transport (Award No. 693JK321NF0002).

Keywords

Complementary sparse representation
Eddy current
Magnetic flux leakage
Nondestructive evaluation

Access to Document

10.23919/ACES57841.2023.10114697

Cite this

Mukherjee, S., Udpa, L., & Deng, Y. (2023). Enhanced Defect Detection in NDE Using Pixel Level Data Fusion. In 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023 (2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACES57841.2023.10114697

Mukherjee, Subrata ; Udpa, Lalita ; Deng, Yiming. / Enhanced Defect Detection in NDE Using Pixel Level Data Fusion. 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023).

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title = "Enhanced Defect Detection in NDE Using Pixel Level Data Fusion",

abstract = "Pipeline systems in service continue to degrade with passage of time due to usage. Magnetic flux leakage (MFL) and Eddy Current (EC) based Non destructive evaluation (NDE) methods are most common for inline inspection (ILI) of the metallic pipelines. Many times, a single measurement technique fails to reveal all the characteristics of the damage due to resolution constraints. This paper presents a novel pixel based fusion of both MFL and EC inspection that can detect real defects like cracks and slits of sub millimeter dimension. An automated data based screening rule is developed to judge whether fusion is needed. Then a transform domain based fusion method called complementary sparse representation (CSR) along with post processing steps is developed for fusion of the source images. The efficacy of the fusion algorithm is studied on both the MFL and EC data generated by simulation means.",

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author = "Subrata Mukherjee and Lalita Udpa and Yiming Deng",

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doi = "10.23919/ACES57841.2023.10114697",

language = "English",

series = "2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023",

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Mukherjee, S, Udpa, L & Deng, Y 2023, Enhanced Defect Detection in NDE Using Pixel Level Data Fusion. in 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023. 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023, Monterey, United States, 03/26/23. https://doi.org/10.23919/ACES57841.2023.10114697

Enhanced Defect Detection in NDE Using Pixel Level Data Fusion. / Mukherjee, Subrata; Udpa, Lalita; Deng, Yiming.
2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023).

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

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AU - Mukherjee, Subrata

AU - Udpa, Lalita

AU - Deng, Yiming

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N2 - Pipeline systems in service continue to degrade with passage of time due to usage. Magnetic flux leakage (MFL) and Eddy Current (EC) based Non destructive evaluation (NDE) methods are most common for inline inspection (ILI) of the metallic pipelines. Many times, a single measurement technique fails to reveal all the characteristics of the damage due to resolution constraints. This paper presents a novel pixel based fusion of both MFL and EC inspection that can detect real defects like cracks and slits of sub millimeter dimension. An automated data based screening rule is developed to judge whether fusion is needed. Then a transform domain based fusion method called complementary sparse representation (CSR) along with post processing steps is developed for fusion of the source images. The efficacy of the fusion algorithm is studied on both the MFL and EC data generated by simulation means.

AB - Pipeline systems in service continue to degrade with passage of time due to usage. Magnetic flux leakage (MFL) and Eddy Current (EC) based Non destructive evaluation (NDE) methods are most common for inline inspection (ILI) of the metallic pipelines. Many times, a single measurement technique fails to reveal all the characteristics of the damage due to resolution constraints. This paper presents a novel pixel based fusion of both MFL and EC inspection that can detect real defects like cracks and slits of sub millimeter dimension. An automated data based screening rule is developed to judge whether fusion is needed. Then a transform domain based fusion method called complementary sparse representation (CSR) along with post processing steps is developed for fusion of the source images. The efficacy of the fusion algorithm is studied on both the MFL and EC data generated by simulation means.

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BT - 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

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Mukherjee S, Udpa L, Deng Y. Enhanced Defect Detection in NDE Using Pixel Level Data Fusion. In 2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023). doi: 10.23919/ACES57841.2023.10114697

Enhanced Defect Detection in NDE Using Pixel Level Data Fusion

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