Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

Mahtab Mohtasham Khani; Sahand Vahidnia; Leila Ghasemzadeh; Y. Eren Ozturk; Mustafa Yuvalaklioglu; Selim Akin; Nazim Kemal Ure

doi:10.1177/1475921719883202

Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

Mahtab Mohtasham Khani^*
, Sahand Vahidnia
, Leila Ghasemzadeh
, Y. Eren Ozturk
, Mustafa Yuvalaklioglu
, Selim Akin
, Nazim Kemal Ure

^*Bu çalışma için yazışmadan sorumlu yazar

Yapay Zeka ve Veri Mühendisliği Bölümü

Araştırma sonucu: Dergiye katkı › Makale › bilirkişi

78 Atıf (Scopus)

Özet

Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	1440-1452
Sayfa sayısı	13
Dergi	Structural Health Monitoring
Hacim	19
Basın numarası	5
DOI'lar	https://doi.org/10.1177/1475921719883202
Yayın durumu	Yayınlandı - 1 Eyl 2020

Bibliyografik not

Publisher Copyright:
© The Author(s) 2019.

Finansman

https://orcid.org/0000-0003-4811-072X Mohtasham Khani Mahtab 1 * Vahidnia Sahand 1 * Ghasemzadeh Leila 1 * Ozturk Y Eren 2 Yuvalaklioglu Mustafa 2 Akin Selim 2 https://orcid.org/0000-0003-2660-2141 Ure Nazim Kemal 1 1 Istanbul Technical University, Istanbul, Turkey 2 General Electric, Istanbul, Turkey Mahtab Mohtasham Khani, Istanbul Technical University, Maslak, Sarıyer, 34467 Istanbul, Turkey. Email: [email protected] * These authors contributed equally to the article. 11 2019 1475921719883202 © The Author(s) 2019 2019 SAGE Publications Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines. Convolutional neural network CNN image processing crack detection gas turbine machine learning deep learning classification computer vision structural health monitoring GE Energy https://doi.org/10.13039/100006774 edited-state corrected-proof Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the General Electric Power Department. ORCID iDs Mahtab Mohtasham Khani https://orcid.org/0000-0003-4811-072X Nazim Kemal Ure https://orcid.org/0000-0003-2660-2141

Finansörler
General Electric Power Department

Belgeye Erişim

10.1177/1475921719883202

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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title = "Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines",

abstract = "Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26\% classification accuracy on a data set of cracked surface images collected from gas turbines.",

keywords = "CNN, Convolutional neural network, classification, computer vision, crack detection, deep learning, gas turbine, image processing, machine learning, structural health monitoring",

author = "\{Mohtasham Khani\}, Mahtab and Sahand Vahidnia and Leila Ghasemzadeh and Ozturk, \{Y. Eren\} and Mustafa Yuvalaklioglu and Selim Akin and Ure, \{Nazim Kemal\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2019.",

year = "2020",

month = sep,

day = "1",

doi = "10.1177/1475921719883202",

language = "English",

volume = "19",

pages = "1440--1452",

journal = "Structural Health Monitoring",

issn = "1475-9217",

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TY - JOUR

T1 - Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

AU - Mohtasham Khani, Mahtab

AU - Vahidnia, Sahand

AU - Ghasemzadeh, Leila

AU - Ozturk, Y. Eren

AU - Yuvalaklioglu, Mustafa

AU - Akin, Selim

AU - Ure, Nazim Kemal

N1 - Publisher Copyright: © The Author(s) 2019.

PY - 2020/9/1

Y1 - 2020/9/1

N2 - Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines.

AB - Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines.

KW - CNN

KW - Convolutional neural network

KW - classification

KW - computer vision

KW - crack detection

KW - deep learning

KW - gas turbine

KW - image processing

KW - machine learning

KW - structural health monitoring

UR - https://www.scopus.com/pages/publications/85074996117

U2 - 10.1177/1475921719883202

DO - 10.1177/1475921719883202

M3 - Article

AN - SCOPUS:85074996117

SN - 1475-9217

VL - 19

SP - 1440

EP - 1452

JO - Structural Health Monitoring

JF - Structural Health Monitoring

IS - 5

ER -

Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

Özet

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

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