IJBTT

Optimizing Rail Track Maintenance by Integrating Geometry Data with Cloud Data Lake and IoT
	© 2024 by IJCTT Journal
	Volume-72 Issue-8
	Year of Publication : 2024
	Authors : Ram Sekhar Bodala, Lakshmana Rao Koppada, Harshavardhan Yedla
	DOI :  10.14445/22312803/IJCTT-V72I8P124

How to Cite?

Ram Sekhar Bodala, Lakshmana Rao Koppada, Harshavardhan Yedla, "Optimizing Rail Track Maintenance by Integrating Geometry Data with Cloud Data Lake and IoT," International Journal of Computer Trends and Technology, vol. 72, no. 8, pp. 164-170, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P124

Abstract
This paper explores the real-time integration of the IBM Maximo Application Suite with Track Geometry Car (TGC) defect inspection systems. It integrates the data streaming of IoT systems by leveraging AWS Cloud Data Lake for capturing and storing data. The integration aims to enhance railway maintenance operations through advanced data analytics and asset management capabilities. By combining TGC’s precise track monitoring and defect detection with Maximo’s robust asset management platform, railway operators can implement more effective and proactive maintenance strategies. This integration facilitates real-time data processing, immediate defect reporting, and streamlined maintenance workflows, ultimately improving the safety and reliability of railway networks.

Keywords
IBM Maximo, Track Geometry Car, Railway Maintenance, Real-time Integration, AWS Cloud Data Lake, IoT, Defect Inspection, Asset Management.

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