TTT NEWS NETWORK
KOLKATA, 3 NOVEMBER 2024:
In a significant leap toward modernizing train operations, Howrah Division has implemented a ground-breaking predictive maintenance system powered by Artificial Intelligence (AI) and Machine Learning (ML). This innovative software, developed at the Jheel Siding Coaching Depot, analyzes real-time Remote Monitoring and Management of Locomotives and Trains (REMMLOT) data to detect potential issues and prevent disruptions. With AI-driven insights, the system ensures timely fault detection, targeted repairs, and smooth train operations, setting a new benchmark for operational excellence.
This intelligent system applies advanced algorithms to monitor multiple operational modes of Vande Bharat (VB) train sets and compare journey parameters with theoretical models. Inverted logic trees and pre-set threshold values help identify faults and alert the maintenance team for corrective action. By analyzing running conditions at the time of fault generation, the software pinpoints defective sub-assemblies, enabling quick intervention. Maintenance staff can further provide feedback on faults, classify their severity, and document corrective measures, creating a continuous improvement cycle.
The integration of Original Equipment Manufacturer (OEM) manuals, previous feedback records, and ML libraries enhances the system’s ability to suggest effective fixes for recurring or critical faults. As the software learns from every input, its accuracy improves, reducing the likelihood of operational interruptions.
The predictive maintenance software has already demonstrated its effectiveness by resolving 22 faults, including issues with Static Inverters (STV), loose connections, Low Tension Control (LTC) modules, relays, and defective speed sensors. These early interventions have prevented enroute detentions and ensured uninterrupted train services. The system also generates daily reports in PDF format, documenting faults, corrective actions, and critical code analysis, ensuring better transparency and streamlined maintenance planning.
With the ability to monitor every component closely, the system not only enhances the reliability of VB trainsets but also holds the potential to be expanded across the Indian Railways network. As more data is fed into the system, its fault detection capabilities will continue to improve, enabling deeper insights and better root cause analysis. Future enhancements will include time trend analysis to identify recurring faults and temperature profiling to monitor sub-assembly performance over time, ensuring optimized maintenance schedules.
This initiative aligns with Indian Railways’ vision of adopting modern technologies to improve operational efficiency and passenger satisfaction. By embracing AI and ML, the Howrah Division is not just preventing faults but also shaping the future of predictive maintenance, setting a new standard in the management of rolling stock.
Advertisement:
Add Comment