Predict breakdown to enhance mobility
An algorithm that predicts when breakdowns are the most likely to occur in order to better program maintenance plans
// KEY RESULTS
Sensors monitor in real time different parameters of the escalator in order to detect anomalies and, through the construction of patterns, predict possible breakdowns to come.
The experimentation took place in Paris.
// PARTNERS INVOLVED
The experimentation took place on an escalator in the Parisian "Gare de l'Est" train station. Partners provided the following data sets: elements of “nominal” escalator functions, including the type of signal (vibration, electrical, audible) that allows for monitoring. The selected startup – Fieldbox.ai - allows the deployment of AI within industrial facilities, in order to optimize and automate the machinery operations.
// PAIN POINT & OPPORTUNITY
Escalators are a central element of transportation in public spaces. When they break down it causes inconvenience to commuters and creates disturbances that can be felt beyond the escalators’ initial location.
Escalators are used globally and the market for escalator is surging (driven by a global urbanization and by an aging population). Disfunctioning escalators directly impact passengers flows in central hubs and affect the quality of life of users