Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal

Non-destructive evaluation (NDE) plays an important role in many industrial fields, such as detecting cracking in steam generator tubing in nuclear power plants and aircraft. This paper investigates on the effect of the depth of the defect, width of the defect, and the type of the material on the ed...

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Main Authors:	Moneer, A. Faraj, Fahmi, Samsuri, Abdalla, Ahmed N., Damhuji, Rifai, Kharudin, Ali
Format:	Article
Language:	English English
Published:	MDPI AG 2017
Subjects:	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/18344/ http://umpir.ump.edu.my/id/eprint/18344/ http://umpir.ump.edu.my/id/eprint/18344/ http://umpir.ump.edu.my/id/eprint/18344/1/Adaptive%20neuro-fuzzy%20inference%20system%20model%20based%20on%20the%20width%20and%20depth%20of%20the%20defect%20in%20an%20eddy%20current%20signal.pdf http://umpir.ump.edu.my/id/eprint/18344/2/Adaptive%20neuro-fuzzy%20inference%20system%20model%20based%20on%20the%20width%20and%20depth%20of%20the%20defect%20in%20an%20eddy%20current%20signal%201.pdf

id	ump-18344
recordtype	eprints
spelling	ump-183442018-10-03T07:37:40Z http://umpir.ump.edu.my/id/eprint/18344/ Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal Moneer, A. Faraj Fahmi, Samsuri Abdalla, Ahmed N. Damhuji, Rifai Kharudin, Ali T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Non-destructive evaluation (NDE) plays an important role in many industrial fields, such as detecting cracking in steam generator tubing in nuclear power plants and aircraft. This paper investigates on the effect of the depth of the defect, width of the defect, and the type of the material on the eddy current signal which is modeled by an adaptive neuro-fuzzy inference system (ANFIS). A total of 60 samples of artificial defects are located 20 mm parallel to the length of the block in each of the three types of material. A weld probe was used to inspect the block. The ANFIS model has three neurons in the input layer and one neuron in the output layer as the eddy current signal. The used design of experiments (DOE) software indicates that the model equations, which contain only linear and two-factor interaction terms, were developed to predict the percentage signal. This signal was validated through the use of the unseen data. The predicted results on the depth and width of defect significantly influenced the percentage of the signal (p < 0.0001) at the 95% confidence level. The ANFIS model proves that the deviation of the eddy current testing measurement was influenced by the width and depth of the defect less than the conductivity of the materials. MDPI AG 2017-06 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18344/1/Adaptive%20neuro-fuzzy%20inference%20system%20model%20based%20on%20the%20width%20and%20depth%20of%20the%20defect%20in%20an%20eddy%20current%20signal.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/18344/2/Adaptive%20neuro-fuzzy%20inference%20system%20model%20based%20on%20the%20width%20and%20depth%20of%20the%20defect%20in%20an%20eddy%20current%20signal%201.pdf Moneer, A. Faraj and Fahmi, Samsuri and Abdalla, Ahmed N. and Damhuji, Rifai and Kharudin, Ali (2017) Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal. Applied Sciences, 7 (7). pp. 1-12. ISSN 2076-3417 http://www.mdpi.com/2076-3417/7/7/668/htm DOI: 10.3390/app7070668
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Moneer, A. Faraj Fahmi, Samsuri Abdalla, Ahmed N. Damhuji, Rifai Kharudin, Ali Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal
description	Non-destructive evaluation (NDE) plays an important role in many industrial fields, such as detecting cracking in steam generator tubing in nuclear power plants and aircraft. This paper investigates on the effect of the depth of the defect, width of the defect, and the type of the material on the eddy current signal which is modeled by an adaptive neuro-fuzzy inference system (ANFIS). A total of 60 samples of artificial defects are located 20 mm parallel to the length of the block in each of the three types of material. A weld probe was used to inspect the block. The ANFIS model has three neurons in the input layer and one neuron in the output layer as the eddy current signal. The used design of experiments (DOE) software indicates that the model equations, which contain only linear and two-factor interaction terms, were developed to predict the percentage signal. This signal was validated through the use of the unseen data. The predicted results on the depth and width of defect significantly influenced the percentage of the signal (p < 0.0001) at the 95% confidence level. The ANFIS model proves that the deviation of the eddy current testing measurement was influenced by the width and depth of the defect less than the conductivity of the materials.
format	Article
author	Moneer, A. Faraj Fahmi, Samsuri Abdalla, Ahmed N. Damhuji, Rifai Kharudin, Ali
author_facet	Moneer, A. Faraj Fahmi, Samsuri Abdalla, Ahmed N. Damhuji, Rifai Kharudin, Ali
author_sort	Moneer, A. Faraj
title	Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal
title_short	Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal
title_full	Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal
title_fullStr	Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal
title_full_unstemmed	Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal
title_sort	adaptive neuro-fuzzy inference system model based on the width and depth of the defect in an eddy current signal
publisher	MDPI AG
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18344/ http://umpir.ump.edu.my/id/eprint/18344/ http://umpir.ump.edu.my/id/eprint/18344/ http://umpir.ump.edu.my/id/eprint/18344/1/Adaptive%20neuro-fuzzy%20inference%20system%20model%20based%20on%20the%20width%20and%20depth%20of%20the%20defect%20in%20an%20eddy%20current%20signal.pdf http://umpir.ump.edu.my/id/eprint/18344/2/Adaptive%20neuro-fuzzy%20inference%20system%20model%20based%20on%20the%20width%20and%20depth%20of%20the%20defect%20in%20an%20eddy%20current%20signal%201.pdf
first_indexed	2023-09-18T22:25:56Z
last_indexed	2023-09-18T22:25:56Z
_version_	1777415966374756352

Adaptive Neuro-Fuzzy Inference System Model Based on the Width and Depth of the Defect in an Eddy Current Signal

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