Agenda Day One

Day One: Wednesday 26 June 2019

Case Studies & Discussions Focusing On Data Utilisation & Predictive Maintenance To: Evaluate Practical Applications To Drive Efficiency & Reduce Costs

8.00  Coffee And Registration

9.00  Chairs Welcome And Introduction

EUROPEAN TRACK OPERATORS PERSPECTIVE: OPTIMISING INFRASTRUCTURE MAINTENANCE 

9.10 Intelligent Rail Infrastructure Maintenance: Today And Tomorrow

Bardo Schettini Gherardini, Senior Analyst and Manager of the Technical Team,   European Rail Infrastructure Managers (EIM)

9.40  Questions And Discussion

DATA UTILISATION SESSION 1: GLOBAL OPERATOR CASE STUDY - GENERATING OBJECTIVE DATA FOR TRACK ANALYSIS 

9.50 The Usage Of Vision System Technology To Make Automatic Patrolling And Periodical Inspection A Reality

The difficulty to keep workforce is expected to emerge in the 2050s in Japan, as the population between the age of 15 and 65 will decrease by 30%. As one of the tactics of the future vision, JR-West intends to make "automatic inspection" a reality. 

  • Improving the European standard algorithm of Vision System technology
  • The right balance between "new-technology" and "past-technics"

Hirotaka Yokouchi, Manager of Track Division, Track and Structures Department of Shinkansen (High-Speed Rail), West Japan Railway Company

10.20  Questions And Discussion

10.30  Morning Refreshments & Networking In The Exhibition Area

DATA UTILISATION SESSION 2 -  OPERATOR CASE STUDY - DIAGNOSING TRACK DEFECTS  

11.00 Optimising Diagnostic Data To Monitor Track Conditions.

Exploring the increased potential of track monitoring technology that enables to save money and have a reliable track structure.

  • Which data is import to use?
  • How to better detect track defects?
  • Technology to report and alert faults to the user
  • Visual Inspection and track geometry

Marco Gallini, Head of Diagnostics and Maintenance Machines Department, Italian Railway Infrastructure Manager (RFI S.p.A.)

11.30  Questions And Discussion

STRATEGIC DATA UTILISATION SESSION 1 - DATA STREAM MANAGEMENT

11.40 Utilise Data Effectively And Stream The Whole System: RILA -Generating The Geodetical Backbone With Continuous Dynamic Data Positioning

Predicting track condition and trends using geodetically-accurate positioned data streams - including engineering data to correct track alignments - to achieve optimum assets reliability and minimise maintenance costs.

  • Using Track Recording Vehicle, (TRV) 
  • Automated Track Measuring System (ATMS)
  • Combining different systems for predictive analysis
  • Resulting engineering data for maximum life of assets

Nick van den Hurk, Principal Consultant Rail Transport, Fugro

12.00  Questions And Discussion

DATA OWNERSHIP - CLOUD STORAGE

12.10 Examine Who Owns The Data In The Cloud & How To Take Functional Data From Different Sources 

The myriad of data collected is an important asset; this session will shed light in the regulatory and commercial issue surrounding data ownership and responsibilities. 

  • How is data stored?
  • Who owns the data?
  • Who is responsible of safe keep of your data?
  • What happen if you want to change providers, will you lose your data?

Joël Casutt, Teamleader Technology and Development,  SBB

12.30 Questions & Discussion

12.40  Lunch In The Networking Exhibition Area

PARTNER CASE STUDY

13.40  Manage Your Assets Smarter - Intelligent Remote Track Condition Monitoring To Ensure 100% Availability

A representative of Siemens Mobility

14.00  Questions And Discussion

NEW TECHNOLOGIES FOR DATA MANAGEMENT - DEEP LEARNING & DATA ANALYTICS TECHNOLOGY

14.10 New Technology Applications For Deep Maintenance Data Utilisation: What's On The Horizon For Rail?

In the last two years an Innosuisse Project in collaboration with CSEM fast-tracked the development of deep learning algorithms for processing images acquired of the rails and their surroundings. Images are collected by  diagnostic vehicles up to 160 km/h and analysed using ultra-modern algorithms to identify and classify defects. As more and more data is stored, the self-learning system uses this knowledge to progressively improve its performance and improves safety and availability of the railway infrastructure.

  • The business case for AI integration
  • What systems are available, and what is the difference between them?
  • Cost benefit analysis
  • What is the time of development?

Joël Casutt, Teamleader Technology and Development,  SBB

Matthias Höchemer, Expert Machine Learning & Vision, Swiss Center for Electronics and Microtechnology (CSEM)

14.40 Questions & Discussion

PREDICTIVE MAINTENANCE SESSION 1 - SHIFTING FROM TRADITIONAL MAINTENANCE TO A PREDICTIVE MODEL

14.50 Explore How To Transform From Regular Maintenance To Predictive Maintenance: Improving Work Practices In the Field

Understanding the right methods and processes for implementing new equipment for main track predictive maintenance and how to correctly implement process changes in an organisation to successfully transition from Generic Time Based to Predictive Based Maintenance.

  • How easy is it to implement?
  • Work orders priorities or watch list?
  • How to prioritise work
  • Reporting back the defect root cause
  • How much will it cost?
  • How long does it take to implement?

15.20 Questions & Discussion

15.30 Afternoon Refreshments & Networking In The Exhibition Area

PREDICTIVE MAINTENANCE SESSION 2: OPERATOR CASE STUDY - REDUCE COSTS THROUGH PREDICTIVE MAINTENANCE

16.00 How To Manage Predictive Maintenance To Reduce Number Of Failures, Amount Of Unplanned Maintenance & Required Level of Reserve Asset Capacity

Detailed real life case study on how a track operator has reduced maintenance cost and failures by successfully applying predictive maintenance best practice.

  • How easy was it to implement?
  • Encountered challenges and related solutions
  • What data are they using and why?
  • Cost benefit analysis

16.30 Questions & Discussion

PREDICTIVE MAINTENANCE SESSION 3 - MAINTAINING A FULLY OPERATIONAL NETWORK

16.40 Combine Optimal Preventative Maintenance With Train Scheduling: Diagnose When To Do The Maintenance 

With shorter and shorter time windows available to perform essential maintenance activities, it is essential to be able to combine state of the art predictive technology with sensible scheduling strategies.  

  • How to work around limited track access
  • Lengthening intervals between maintenance actions (without affecting services)
  • Ensuring durability of new parts
  • Fix or replace?

17.10 Questions & Discussion

17.20 Chair's Closing Remarks

17.30 - 18.30 Networking Drinks Reception In The Exhibition Area

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