“The evolution led us to enlarge the data we were able to capture, and this in turn opened doors to new internal and external products, allowing us on daily basis to improve our service for our clients and passengers."
Since its formation in 1938, the Société nationale des chemins de fer français (SNCF), France’s national state-owned railway company has had a keen focus on innovation. They are of course best known for their high-speed train, the Train à Grande Vitesse (TGV) that has been in operation since 1980, but other pioneering developments include their ‘broken nose’ style of electric and diesel locomotives designed by the famous Paul Arzens, being the world’s first operator to offer carbon neutral travel at no extra cost to passengers and code-sharing with airlines. Within the SNCF group, it is SNCF Réseau who is responsible for the management and maintenance of the huge railway network which includes 30,000 km of line – over 2,000 of which is high-speed, 15,000 level crossings, 2,200 signal boxes, 1,576 tunnels and 1,201 foot bridges among many other assets.
Encouraging mobility and supporting the ensuing increase in traffic is one of the main missions of SNCF Réseau. Over the last 10 years, passenger transport has increased by 50% and is forecast to continue to grow. The French railway network already has a very high level of performance, but has several opportunities for further improvement, and today we focus on one of the most important - its modernization and digitization program to increase the utilization availability of its asset and better plan maintenance and investments. Today our Editor, Luke Upton exclusively to Stéphane de Paris, Chief Data Officer of SNCF Réseau and Matt Miller, OSIsoft’s Industry Principal for Transportation.
SNCF Réseau ( @SNCFReseau ) is in full transformation mode at the moment, with one of their key aims to optimise the maintenance of the network. “Key to this is to improve monitoring, carried out through; remote monitoring deployment, new technologies such as sensors, wireless and the internet of things and video technology both fixed and on-board. All this means we have to have tower control, with supervision centres deploying a new management process across the network. And this new approach to maintenance limits the impacts on traffic, helps us better anticipate incidents and improves our asset management by being able to define and adjust degradation curves, and capitalize on asset knowledge to define new processes” states de Paris.
To achieve this improved approach to maintenance SNCF Réseau in 2014 formed a partnership with OSIsoft ( @OSIsoft ), global leaders in turning data into information, first in Île-de-France and now rolling out through the whole country.
At that heart of this partnership is OSIsoft’s PI System, and Matt Miller tells us more about what it offers. “For rail and metro operators, how they access and learn from the data that resides in multiple, often incompatible systems across their network is a complex problem. These data sources all compete for attention, and in a changing industry with ever increasing amounts of connected devices and data streams this is only increasing. It is as a result easy to miss crucial data.”
“For 35 years across numerous other industries, the PI System has been supporting better data analysis - it offers a simple solution, that is quick and easy to install, collect data from and build upon. Quite simply it connects the data from all your machines and equipment to the people who need to use it. And in rail and metro, it is most powerful in helping operators realise the power of their own real-time data to make their maintenance more efficient” continues Miller.
By using the PI System, SNCF Réseau have been able, through one single accessible platform, to rediscover their assets leverage 10+ years of legacy data collected in the field, set up and deploy new analyses and tools, designed and implemented by and for the maintenance teams, and open up a whole new perspective on smart asset management.
de Paris shares some details; “The evolution led us to enlarge the data we were able to capture, and this in turn opened doors to new internal and external products, allowing us on daily basis to improve our service for our clients and passengers. Before PI System was deployed, the data would be sourced in the field processed within SCADA and the limited information then passed onto the remote monitoring centre and through to field maintenance via telephone.
Now, with PI, the field data is allied with achieve data through SCADA allowing a fuller analysis which can then be more easily shared with the maintenance teams through new devices.”
The co-operation with OSIsoft can be plotted on SNCF Réseau’s maintenance policy maturity beginning with corrective maintenance moving onwards to preventative maintenance (based on running hours or calendar-based) then condition based maintenance (based on measurements) and now predictive maintenance (extrapolated predictions based on analyses / assessment of data related to wear).
“SNCF RESEAU is demonstrating their leadership in digitalization and has been visionary in bringing all their stakeholders on the digital journey around industrial data. The maintenance and asset management teams and their needs are at the center of everything. OSIsoft believes this approach will expedite adoption and is pleased to be supporting them in reaching their operational goals” concluded Matt.
And this partnership is essential in helping SNCF Réseau achieve their ambitious goals to further modernise the network throughout France, integrate the LGV high-speed lines into the national railway network and ensure that their 15,000 daily trains, 5 million passengers and 250,000 tonnes of freight they transport are part of fast, on-time and comfortable journeys.
For more on how OSIsoft work with the rail industry click here or contact firstname.lastname@example.org Want to make better use of your available data, and see how mature your current use is, OSIsoft offer an assessment and receive a benchmark report to create some momentum towards your own transformative data strategy.
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