IoT & Maintenance Stream

 

FEATURED SPEAKERS

 

Nalinaksh Vyas

Stephane Callet    , Signalling project Director,    SNCF

 

 

Juliette Marais

 

 

Andrew Morsley  , Head of Maintenance Modernisation,  TFL

 

Christian Schlehuber

Christopher McMorrow  , Head of Fleet Management,  Irish Rail

 

Juergen Mues

Jurgen Mues  , Head of Asset Management,  SBB Cargo

 

 

S

IOT & Maintenance, Wednesday 18 April 2018

CBM CASE STUDIES

Christopher McMorrow
IOT & Maintenance
12:10

Rolling out condition based maintenance in Ireland

  • Key lessons from rolling out a remote diagnostics facility and condition based maintenance: what would we have done differently?
  • Reading your data reliably – how can rail operators optimise the data they have on their rolling stock to see the signs that maintenance is needed?
  • Moving from having a back-up maintenance plan to relying on sensor data – how can rail operators be sure in their trust of the data?
IOT & Maintenance
12:30

Developing real time data flows to optimise maintenance

  • Collating on-board real-time data effectively you will be able to schedule pro-active maintenance at the best time 
  • Ensuring your investments ultimately improving the reliability of your fleet and benefiting from new insights into your rolling stock 
  • Understanding the opportunities in saving time and budget with the impact of a real-time data approach 
IOT & Maintenance
12:50

Taking a que from Aviation: the potential for CBM in rail

  • Describing VR Group's CBM project and the impact already made in our network 
  • Refining our algorithms and improving the impact of our data over the years 
  • Sharing our most effective projects and the reach of our project. 
13:10

Networking Lunch

DATA INSIGHTS FOR ROLLING STOCK

Andrew Morsley
IOT & Maintenance
14:30

Using Big Data to gain insights in the maintenance of our rolling stock

  •     Hear how TFL are taking advantage of increasing number of sensors and sources of data on our rolling stock
  •     Understanding how big data can be optimised and developing new algorithms to gain new insights into our assets
  •     Key case studies: simply changes to lower the risk of disruption and the cost of maintenance 
Alvaro Zevallos
IOT & Maintenance
14:45

Remote and Real-Time Diagnostics of Rolling Stock Assets Condition: Business Cases and Main Results

  • How can technical and financial risks be minimised with the implementation of highly precise asset management?
  • What best practices should be used for this purpose to optimise the impact on rail networks?
  • Sharing insight on the qualitative and quantitative benefits of applying big data techniques to rolling stock assets to improve performance
Nalinaksh Vyas
IOT & Maintenance
15:00

Developing a big data platform to optimise operations on the Indian Railways Network

  • Understand how Indian Railways is working to develop a big data for asset management
  • Exploring the deep learning paradigm the rail industry
  • Developing an outlook of applications and innovations for a big data platform
IOT & Maintenance
15:20

Using digital innovation to empower Deutsche Bahn's fleet

  • Overcoming the challenges of building data in one of the world’s biggest rail networks
  • Working to establish effective use cases which can be extended further
  • Overcoming the challenge of culture: one of the biggest challenges facing the success of predictive maintenance
IOT & Maintenance
15:40

Panel debate: How can rail capitalise on the potential opportunities arising from condition based maintenance?

Themes include:
  • Discussing the main challenges that rail operators and network managers face when first implementing CBM
  • Ensuring you are gathering and optimising data: what are the key lessons that can be shared?
  • Developing and building on key insights to optimise data and CBM
last published: 22/Nov/17 15:05

IOT & Maintenance, Thursday 19 April 2018

INTERNET OF TRAINS & AI

Jurgen Mues
IOT & Maintenance
11:00

IoT technology on Switzerland’s railways

  • How to produce sustainable asset management for long life track and trains
  • Leveraging big data-backed asset management for an efficient network
  • Creating an innovative asset management policy to ensure digital is at the core of SBB Cargo
Ander Azkarate
IOT & Maintenance
11:15

How is Danobat’s advanced technology leveraging highly accurate wheel measurements and wheel reprofiling systems for maximum operating efficiency

  • How non-stop data is helping operators to make smart maintenance a reality
  • Technological innovation to simplify data maintenance
  • How to reduce inspections and maximise interventions
IOT & Maintenance
11:30

How operators are using IoT and big data to transform maintenance

  • Challenges when implementing IoT in maintenance
  • Learning to distrust predefined thresholds
  • Lessons to be drawn from a mature predictive maintenance system
last published: 22/Nov/17 15:05