New Delhi [India], March 20 : In a decisive move to strengthen accountability and performance oversight, Indian Railways has compulsorily retired six officers from service under Rule 1802(a) of the Indian Railway Establishment Code (IREC), Volume 2.
Retiring officers held positions including CME/Project/HQ, Northern Railway; NF-HAG/IRSME, SWR; SAG/ISRE, SECR; SAG/IRSSE, ER; Grade-1 (Under Secretary/Deputy Director), RBSS; and PPS, RBSSS
This decisive action reflects Railways' firm commitment to maintaining operational efficiency and accountability within its ranks. Rule 1802(a) empowers the administration to retire officials in the public interest, and this move signals that non-performance and inefficiency will not be tolerated at any level of the organisation.
Railway officials and employees are expected to take serious note of this development, as the administration reaffirms its zero-tolerance stance toward those failing to meet required service standards.
Earlier, Indian Railways is actively enhancing its safety and operational efficiency through the deployment of advanced Artificial Intelligence (AI) and Machine Learning (ML) devices and smart monitoring systems.
Key initiatives include the development of the TRI-Netra system by the Research Designs and Standards Organisation (RDSO) to assist loco pilots in foggy and adverse weather conditions, and the installation of 24 Wheel Impact Load Detectors (WILD) systems and 25 Online Monitoring of Rolling Stock (OMRS) systems for real-time monitoring of wheel and bearing health. This system comprises optical cameras, infra-red camera and ranging devices (e.g. Radar/Lidar) & AI to create a real-time, enhanced vision system for assisting Loco pilots.
Machine Vision Inspection Systems (MVIS) and Integrated Track Monitoring Systems (ITMS) are also being utilised on a pilot basis for detecting defects in moving trains and railway tracks, respectively. The goal is to leverage technology for better track maintenance planning, improved reliability of assets, and overall operational efficiency.
MVIS is an Artificial Intelligence (AI)/ Machine Learning (ML) based system which generates alerts on detecting any hanging, loose or missing components of moving trains. Three (03) MVIS have been installed in Northeast Frontier Railway, two (02) in Dedicated Freight Corridor Corporation of India Limited (DFCCIL) and one (01) in South East Central Railway on a pilot basis for freight stock. Further, an MoU has been signed between IR and DFCCIL to induct four (04) MVIS over the IR network for freight stock. Also, the Research Designs and Standards Organisation (RDSO) has taken up the development of MVIS for rolling stock in collaboration with the industry through an Expression of Interest (EoI).
Along with that, 3 Integrated Track Monitoring Systems (ITMS) were deployed for AI-based Inspection and Monitoring of Track Components.
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