Maha Metro to leverage AI, IoT platformsfor better Nagpur Metro services

 

 

 

 

 

With 40 km of track in Phase-I of Nagpur Metro totally operational, Maharashtra Metro Rail Corporation Limited (MMRCL) is planning for predictive maintenance to enable reliable services to the commuters. For the same, MMRCL proposes to tap into Internet of Things (IoT) that enables data analytics to sift through massive data generated through daily operations. This would ensure better working and avoid failures either due to malfunction of Over Head Equipment (OHE) or any other mechanical aspect. For the same, Metro will be procuring latest software for helping with advanced analytics and visualisation of complex scenarios. The analytics act as catalyst in helping to take decision at right time by enabling various possible scenarios thus ensuring interruption-free run of metro services.

 

Through Artificial Intelligence (AI), Metro would leverage Machine Learning (ML) techniques to analyse sensory data. Also, past maintenance records would be assessed for identifying patterns to predict potential equipment failures in future. The same would enable predictive maintenance strategies for critical assets and equipment in the metro system, ensuring their reliable operations and minimising failures, said Akhilesh Halve, Deputy General Manager, Corporate Communications. For example, monitoring the health of wheels sets bearing is most critical as these components suffer wear and tear due to friction and timely repair of them is must. For the same, vibration sensors would be affixed and data coming from them would be analysed using Machine Learning algorithms that identify patterns indicative of bearing degradation. Thereafter, monitoring of track switches enable trains to move between different tracks, and its constant use causes stress on points and crossings.

 

The data from track side monitoring systems has information on train movement and help identify patterns of switches wear and tear. Also, monitoring of overhead power supply system, analysing faults through sensor data on voltage fluctuations, current surges and weather conditions would ensure maintenance teams are on alert. Traction motors are susceptible to overheating and electrical faults and analysing motor current, temperature and vibration data, one can pinpoint patterns indicative of motor degradation and avoid potential failures. Then, door malfunction is an important safety aspect and AI would monitor opening/closing times, number of cycles (openings/closings), and sensor data on motor current and temperature using Machine Learning models. Further, keeping track on functionality of emergency brakes is critical from the point of view of passenger safety is also on cards. Analysing data points from pressure readings to pneumatic braking system and sensor data on brake pad aims to detect any potential issues with the brakes and ensure their working in suitable manner. In a nutshell, maintenance teams would be in a better position to schedule replacements of parts before a failure occurs using the analytical studies. IoT signifies usage of advanced software to help crunch daily data. In next two to three years, Nagpur Metro’s Phase-II would add 43.8 km of track.

 

As to data analytics, Metro officials stated that very complex operations are involved in train operations as several dynamics come into play due to involvement of multiple equipment, machinery, which must work in tandem. As network gets expanded, there is massive number of machinery and helping to keep track of their maintenance schedule is equally important. So with data analysis of the daily operations, the technical team would get latest status as to health of each of the machine part. Metro O&M digital platform and SAP Enterprise Asset Management (SAP EAM) generate massive maintenance data. In addition, operational data from operational technology like SCADAs, TCMS, BMS, etc.. need thorough analysis.

 

For the same, IoT and advanced analytics come into play as Metro officials is keen to prepare various predictive modules to help keep trains running without any interruption. The same is possible as, with IoT, one can get various charts that predict different scenarios, providing seamless co-relating among system, and reasoning in case of deviation from operating standards. Predictive & Reliability Analytics: Post analysis of voluminous data, Maha Metro would leverage its assets and machinery for best use ensuring that commuters gets world-class service, added Halve and for it, predictive and prescriptive maintenance strategy are must for keeping the organisation’s assets and equipment in best possible condition.

 

The technical team would keep inventory ready as they would know when particular equipment will be required. Further, various scenarios put up through advanced analytics offer options in solutions. The software will provide detailed insights into key event metrics like Mean Time Between Failures (MTBF) and Mean Time To Respond (MTTR), helping to track the availability and reliability of assets and equipment components, enabling Maha-Metro to monitor and improve their maintenance operations. Analytics will provide an alert summary for systems, highlighting sensor issues and potential problems. It will also offer active root cause analysis, detailing failure modes; root causes, responsible tags and recommended actions.