PARTNERS 2018

Siemens

PREDICTIVE MAINTENANCE PARTNER

DB

IoT PARTNERS

IBM Tyrens

CO-SPONSORS

Fugro

Close Networking Opportunities

Meet Senior Decision Makers From:


  • Network Rail
  • CrossRail
  • Transport for London
  • Banedanmark
  • Ministry of Transport & Communications
  • Athens Urban Rail Transport

With The Following Job Titles...


  •     Head of Track
  •     Director of Maintenance
  •     Asset Managers
  •     Track Section Managers
  •     Safety Managers
  •     Head of Quality
  •     Track Maintenance Engineer
  •     Route Asset Manager
  •     Infrastructure Programme Director
  •     Senior Project Manager
  •     Maintenance Performance & Reliability
  •     Programme Improvement, Integration & Delivery
  •     Reliability Improvement
  •     Asset Management Competence

Dr Sin Sin Hsu

Head of Track Engineering

Network Rail (High Speed 1) Ltd

Sin Sin acts as the Technical Authority for all track engineering matters on HS1, relating to maintenance, renewal and operation of the High Speed network with the aim of delivering the best High Speed rail customer experience.

Sin Sin is a recognised technical expert in Railway Track Engineering; with particular focus on Switches and Crossings (S&C) and Vehicle/Track Interaction.

A Mechanical Engineer with 20 years of professional work and consultancy experience.  She started in academia as a researcher into structural crashworthiness followed by working as a consultant on vehicle dynamics modelling.  After moving to Network Rail, she progressively gained experience in track renewals, maintenance and HQ functions.  She is a technical leader motivated by delivering effective and sustainable solutions for customers; and passionate about safety by design.

Sin Sin is a member of various technical standards committees and sits on industry steering groups for research projects led by leading UK research institutions.

Sin
TMS EB

Key Themes

Sessions will include:

Utilising Predictive Maintenance And Implementing Innovative New Technologies
Sharing Results On Successfully Predicting Infrastructure Failures
Handing And Managing Big Data To Predict Infrastructure Deterioration
Evaluate Infrastructure Inspection Methods And Ensure Quality Assurance And Control
Utilising Data To Reduce Manual Surveys And Infrastructure Inspection
Artificial Intelligence And Machine Learning For Deep Maintenance Data Utilization
Integrating IOT Applications
Detect And Predict Climate Related Track Events And Increase Security & Safety Of Transport
Trackside Monitoring Technologies
Automation and Robotics Technologies For Inspection
Predictive Analytics Of Switches And Crossings
Efficient Planning Of Tamping
Planning Tools To Improve Manpower Scheduling And Reduce Workman Fatigue
Developing Safe Working Strategies
Successful Application of Mobile Maintenance Trains and Other Mobile Applications in the European Rail Industry
Asset Management And Management Of Deterioration
Outsourcing Or In-House? How To Get The Best Possible Yield From The Market
Continuous Improvement On Managing Human Factors And Safety in Track Maintenance
Building Collaboration And Communication Links Between European Railway Infrastructure Organizations

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