A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers

Due to repeated heavy loads, environmental conditions and non-frequent monitoring, the rail is subjected to heavy damage resulting in sudden failure. Hence, a frequent, faster, and efficient monitoring strategy is required. This paper attempts to investigate the application of guided wave (GW) gener...

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Autores principales:	Harsh Mahajan, Sauvik Banerjee
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Rail inspection Guided wave Surface bonded sensors Rail head damage detection Machine learning application Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/98535892364b4c4f8a97ed49e1f521f5
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spelling	oai:doaj.org-article:98535892364b4c4f8a97ed49e1f521f52021-11-30T04:17:56ZA machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers2666-827010.1016/j.mlwa.2021.100216https://doaj.org/article/98535892364b4c4f8a97ed49e1f521f52022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021001080https://doaj.org/toc/2666-8270Due to repeated heavy loads, environmental conditions and non-frequent monitoring, the rail is subjected to heavy damage resulting in sudden failure. Hence, a frequent, faster, and efficient monitoring strategy is required. This paper attempts to investigate the application of guided wave (GW) generated through surface-bonded piezo-electric wafer transducer (PWT) to detect damages in rail at high frequencies. Firstly, a combined experimental and simulation study is presented in an effort to understand the dispersion characteristics of guided wave and its interaction with head damages in a relatively small rail specimen. The numerical simulation results are validated with those obtained from the experiments showing a good agreement between them. Secondly, a framework based on the machine learning algorithm is proposed to efficiently detect damage in rail head. Numerous inseparable guided wave modes are observed at higher frequencies implying the inability to detect damage through specific mode. Therefore, a machine learning framework is trained using time, frequency, and time–frequency domain features of the signal. Total 672 numerical simulations of different types of damage with different severity and location in the rail head are carried out to train and validate the model. It is found that GW generated through surface bonded PWTs is able to detect minimum defect size of 5% of head area with 1 mm thickness. Finally, the proposed framework is tested using simulation and experiment results of arbitrary damage in the rail head. The error in estimating severity was found to be in the range from 2.00% to 16.67%.Harsh MahajanSauvik BanerjeeElsevierarticleRail inspectionGuided waveSurface bonded sensorsRail head damage detectionMachine learning applicationCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 7, Iss , Pp 100216- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Rail inspection Guided wave Surface bonded sensors Rail head damage detection Machine learning application Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Rail inspection Guided wave Surface bonded sensors Rail head damage detection Machine learning application Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Harsh Mahajan Sauvik Banerjee A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
description	Due to repeated heavy loads, environmental conditions and non-frequent monitoring, the rail is subjected to heavy damage resulting in sudden failure. Hence, a frequent, faster, and efficient monitoring strategy is required. This paper attempts to investigate the application of guided wave (GW) generated through surface-bonded piezo-electric wafer transducer (PWT) to detect damages in rail at high frequencies. Firstly, a combined experimental and simulation study is presented in an effort to understand the dispersion characteristics of guided wave and its interaction with head damages in a relatively small rail specimen. The numerical simulation results are validated with those obtained from the experiments showing a good agreement between them. Secondly, a framework based on the machine learning algorithm is proposed to efficiently detect damage in rail head. Numerous inseparable guided wave modes are observed at higher frequencies implying the inability to detect damage through specific mode. Therefore, a machine learning framework is trained using time, frequency, and time–frequency domain features of the signal. Total 672 numerical simulations of different types of damage with different severity and location in the rail head are carried out to train and validate the model. It is found that GW generated through surface bonded PWTs is able to detect minimum defect size of 5% of head area with 1 mm thickness. Finally, the proposed framework is tested using simulation and experiment results of arbitrary damage in the rail head. The error in estimating severity was found to be in the range from 2.00% to 16.67%.
format	article
author	Harsh Mahajan Sauvik Banerjee
author_facet	Harsh Mahajan Sauvik Banerjee
author_sort	Harsh Mahajan
title	A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
title_short	A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
title_full	A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
title_fullStr	A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
title_full_unstemmed	A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
title_sort	machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/98535892364b4c4f8a97ed49e1f521f5
work_keys_str_mv	AT harshmahajan amachinelearningframeworkforguidedwavebaseddamagedetectionofrailheadusingsurfacebondedpiezoelectricwafertransducers AT sauvikbanerjee amachinelearningframeworkforguidedwavebaseddamagedetectionofrailheadusingsurfacebondedpiezoelectricwafertransducers AT harshmahajan machinelearningframeworkforguidedwavebaseddamagedetectionofrailheadusingsurfacebondedpiezoelectricwafertransducers AT sauvikbanerjee machinelearningframeworkforguidedwavebaseddamagedetectionofrailheadusingsurfacebondedpiezoelectricwafertransducers
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A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers

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