High-speed trainline monitoring and predictive maintenance

Key objectives

  • Analyze the unavailability factors 
  • Follow-up, understand and anticipate incidents
  • Monitor material health to enable predictive maintenance

Key figures

  • +10 data sources coming from SNCF and Vinci
  • 15% better incidents detection
  • 1st delivery: 2 iterations of 1 month each

Context

Vinci, through its subsidiaries Mesea-Lisea, has obtained in 2010 the concession for the LGV line operation (High Speed Line between Tours and Bordeaux, France). The achievement of contractual commitments with railway operators (SNCF today, other potential actors in the future) requires fine infrastructure management and the processing of large volumes of data. This high-stake service level concession comes with high penalties, and therefore, the incidents management and their number have a big impact on the railway operations’ profitability.

As a long-time maintenance and material management expert, it was also critical for the company to accelerate its digital transformation and reinvent the management of its digital assets to be able to focus on its core business: railway infrastructure.

Challenge

Aggregating various data sources

Stakes are multiple: counting and identifying trains, infrastructures availability, responsibilities sharing when an issue arise (rail incident or disabled train), maintenance plan optimization by detecting weak early signals.

To be as simple and efficient as possible, the solution had to integrate more than 20 data sources, including:

  • Data coming from communication between SNCF control center and trains
  • IoT data
  • Meteorological sensors
  • Data from the maintenance train controlling various elements of the infrastructure

Operationalizing data processing

Although all this information already existed, it was completely fragmented between the different departments. This limited approach did not help to fully leverage and power data or to go further by developing new services based on Artificial Intelligence.

Solution

After a thorough evaluation of different solutions, Vinci chose ForePaaS and its partner Eleven, a European strategy firm with a strong expertise on digital and data challenges, for several reasons:

  • To access every component required for a data project, all in one place: collection, storage, exposition, training and run of AI models
  • To leverage a platform with the capability to connect and process multiple data sources and formats, including high-frequency IoT, passive and active data
  • To benefit from an agile collaboration framework with fast iteration cycles and strong support throughout each step of the process

A first demonstrator has been deployed in less than 6 months to better understand the origin of unavailabilities, with a focus on train switches for which data are harder to process. A second phase was launched in the spring of 2019, focusing now on data extraction and predictive models development to move towards more predictive maintenance.

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