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24To enhance the precision of the EIMS, each roller in an escalator is tagged with a tiny radio-frequency identification chip. This innovation enables the system to pinpoint exactly which roller needs attention, making maintenance more efficient and targeted than ever before. %u201cIt%u2019s like giving lifts and escalators a comprehensive health monitoring system,%u201d Prof. Tam adds. %u201cWe can now predict when a part will fail and replace it before it causes any problems. This predictive capability translates into fewer breakdowns, reduced downtime, and ultimately safer rides for everyone using these vertical transportation systems.%u201dPredictive maintenanceTo enable remote management, Prof. Tam and Dr Liu designed a userfriendly smartphone app. This displays a health index based on real-time data collected by the sensors, using colour-coded indicators for overall health and individual component conditions %u2013 green for healthy, yellow for deteriorating, and red for poor. This visual representation allows maintenance personnel to quickly assess the status of each system and prioritise their tasks.A key advantage of both predictive maintenance systems is their ability to learn and improve over time. By leveraging big data analytics and machine learning, the LIMS and the EIMS can predict future degradation and failure with increasing accuracy. The intelligent escalator comb plate equipped with FBG sensors, for example, can predict faults with a remarkable 95% accuracy. PULSE OF THE CITY