Bengaluru: Namma Metro's Yellow Line To Set New Standards With AI-Based Track Monitoring, Here's All About It

Bengaluru Metro is embracing artificial intelligence (AI) to oversee tracks on the Yellow Line, which stretches from RV Road to Bommasandra.

The Yellow Line is pivotal, linking RV Road to Electronics City, a hub for major companies like Infosys and Biocon, and is slated for operation by September 2024.

According to a report from MoneyControl, a senior official from the Bangalore Metro Rail Corporation Limited (BMRCL) stated, “Before starting revenue service, we conduct a trial run with track and third-rail maintenance staff on board. They visually inspect the track and third rail.”

This initial trip, known as the pilot train, includes employees from the rolling stock, traction, signalling, and track maintenance departments.

It operates at a reduced speed to meticulously check all obstacles and potential issues, as per B L Yashwanth Chavan, BMRCL Chief Public Relations Officer (PRO).

The monitoring process mainly relies on a camera-based system, transmitting track images to a server. AI then generates alerts.

In the event of a significant problem, an alert is directly sent to the Operation Control Centres (OCC) via the Train Control and Management system (TCMS).

Chavan, the PRO, emphasised the significance of AI in detecting track issues using advanced technology, reducing the risk of human error.

This marks BMRCL's debut of the Communications-based Train Control (CBTC) signalling system, commonly known as ‘driverless technology’.

Although driverless operation is feasible on the Yellow Line, BMRCL has chosen to continue with loco pilots.

Chinese company CRRC Nanjing Puzhen Company Limited won the contract to supply 216 coaches in December 2019, comprising 126 DTG coaches for the Purple and Green lines and 90 CBTC coaches for the Yellow Line.

Alstom India's managing director (signalling and infrastructure), Thameem Kamaldeen, recently highlighted the role of AI and data analytics in railway operations.

He also emphasised how AI-driven cameras can monitor track conditions, detect anomalies, and prevent derailments.

Kamaldeen also discussed the potential of predictive maintenance in the railway industry, where AI analyses real-time data to identify potential hazards, ensuring precise repair planning and minimising unexpected interruptions, downtime, and repair costs.

He further added that big data and AI-based automated solutions can track radio signal performance, predict failures, and optimise maintenance activities in real-time.