Predictive maintenance on city infrastructure to optimise costs and enhance quality of life
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Backgrounder

Technicians servicing street lights, traffic lights and other street furniture typically follow predetermined schedules. They work on street furniture at standard intervals, which are based on estimates of each piece of equipment’s state of wear.

Data that recently became available from systems connected to this furniture, however, has opened the door to detecting and anticipating maintenance work requirements. Doing this serves two purposes: optimising work (and possibly predicting breakdowns) and enhancing quality of life in cities by reducing the number of unlit areas.

The problem

How to optimise and predict street furniture maintenance by analysing data generated by the furniture or outside databases.

Use cases and experimented fields

The experiment’s success will be measured in terms of the breakdowns identified by analysing data generated by street furniture compared to the anomalies recorded by teams tasked with repair work on the ground.

Over time, the goal is to shorten repair timeframes, reduce the number of malfunctions reported by residents, and reduce the number of unforeseen breakdowns.

The field available for this experiment encompasses all the street lighting in Paris. For the purposes of the experiment, however, only a restricted (but representative) sample of streets and areas will be used.

Business opportunities for the stakeholders

EVESA,  a company related to Bouygues Energies & Services, is tasked with operating and servicing public lighting in Paris and has access to the data gathered by 16,000 units controlling those lights. Its goal is to reduce operating costs while offering new services.

Paris City Council is keen on providing its people with a high-quality and efficient public service.

From a more general perspective, any operator handling electric infrastructure – including EDF as well as many other players outside France – may be interested in optimising predictive maintenance on its facilities. The opportunities to use this experiment’s outcomes, in other words, are not limited to street furniture but encompass any device connected to a control unit and requiring maintenance.

 

data sets
  • Hourly data from 16,000 units controlling street lights
  • Georeferenced data on street lighting in the experiment area
  • Outside data such as weather information
partner experts

BYES:

  • Denis Perrot and Maxime Billot
    Head of Expertise, Large Projects and Systems; Head of Methods
  • Jean Bernard Sers
    Head of Development, Smart Grid, Smart Cities & IOT