Structural health monitoring of railway tracks using IoT-based multi-robot system

Iyer, S; Velmurugan, T; Gandomi, AH; Noor Mohammed, V; Saravanan, K; Nandakumar, S

Structural health monitoring of railway tracks using IoT-based multi-robot system

Iyer, S Velmurugan, T Gandomi, AH Noor Mohammed, V Saravanan, K Nandakumar, S

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2021, 33, (11), pp. 5897-5915
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Iyer, S
dc.contributor.author	Velmurugan, T
dc.contributor.author	Gandomi, AH
dc.contributor.author	Noor Mohammed, V
dc.contributor.author	Saravanan, K
dc.contributor.author	Nandakumar, S
dc.date.accessioned	2022-03-31T03:25:08Z
dc.date.available	2022-03-31T03:25:08Z
dc.date.issued	2021-01-01
dc.identifier.citation	Neural Computing and Applications, 2021, 33, (11), pp. 5897-5915
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/155756
dc.description.abstract	A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-020-05366-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Structural health monitoring of railway tracks using IoT-based multi-robot system
dc.type	Journal Article
utslib.citation.volume	33
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-31T03:25:06Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	11

Abstract:

A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.

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http://hdl.handle.net/10453/155756