UG24 – escalertor

Escalator is a common facility in Hong Kong, it provides transportation service for people to move between levels every day. However, Several existing problems have been realized, leading to accidents and imposing limited flexibility. For example, although most of the escalators are only supervised annually, accidents may still occur due to sudden mechanical failure or other factors, resulting in severe injuries, ranging from slips and falls to even amputation. According to the data from EMSD, an average of 85 escalator incidents are reported annually and it may cause up to 18 casualties and affect over 120 people in one single incident. Therefore, it is essential for property owners to monitor the escalators’ conditions not only regularly, but also effectively. With the help of data logging and AI systems, the monitoring and emergent reacting actions can be automated, thus tackling the existing problems as aforementioned. By analyzing past accidents, we identified some components of the escalator that are the root cause of the mechanical failure. We thus propose to install detectors around the major components to allow continuous monitoring. Escalator data will then be transmitted to a district centralized spot and will be monitored both automatically and manually. AI will learn the patterns of mechanical failure to detect signs of possible failure such as minor component dislocation, and simultaneously predict possible failures and alert the monitoring staff. Reference Link: 1. https://www.emsd.gov.hk/en/lifts_and_escalators_safety/publications/contractors_performance_rating/reported_escalator_incident_records/ 2. https://www.emsd.gov.hk/filemanager/tc/content_794/Langham_Technical_Investigation_Report(Chi).pdf