Year: 2024
Type: article
Abstract: The railway infrastructure is a perfect blend of all branches of engineering. Technology has drastically increased, mainly in the Signalling, Civil, Electrical and Mechanical engineering streams. In the field of Signalling, it has leaped from Mechanical to Electronics Interlocking. Civil engineering has gone from manual track maintenance to high-end mechanized tools. In the field of mechanical engineering, it has progressed from wooden coaches to modern designed (LHB)coaches. In electrical engineering, the technology changed from steam loco to diesel and later electrically powered loco design. Nowadays, the loco is powered by nonconventional sources (like solar or wind). Hence, the maintenance of rail infrastructure with traditional methods of physical supervision is sluggish and prone to frequent failures. However, condition monitoring through manual verification by railwaymen helps in the upkeep of railway assets, but it is prone to human errors and subsequent failure of systems, which may result in disasters like derailments or head-on collisions. Signalling components (Point, Tracks, Signals, OFC, DNS, Dataloggers, EI, Block-working instruments etc.), Mechanical rolling stock (Carriage and Wagon), Electrical fitments (OHE, AT, DG, TSS/SP/SSP, locomotive etc.) and Civil Structures (Bridges, Culverts, rail-tracks, rail beds) all require regular maintenance. This paper introduces a “condition monitoring method Enhanced with Wireless Sensor Network” for railway infrastructure. It also clearly distinguishes the typical category of sensors, which are best suitable for condition monitoring of various types of railway assets. The proposed WSN-based technology will improve railway subsystem reliability, effectively reducing failure time and improving the operating ratio.
Authors: Rajev Kumar Sharma, Ajay Sharma, Sandeep Kanaujia
Institution: United University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405945771