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TRIMIS

Dynamic Diagnosis and Predictive Maintenance of Train Onboard Systems

DIADEM

Dynamic Diagnosis and Predictive Maintenance of Train Onboard Systems
Original Language Title: 
DIAgnostic Dynamique et maintenance prévisionnelle des systèmes en station et EMmbarqués sur train

Background & policy context: 

The components of railway rolling stock are integral parts of the railway "system". The major challenge for operators and manufacturers is the availability of trains to increase user satisfaction without increasing hardware costs and meet the requirements of regularity of organizing authorities. By developing innovative tools for the diagnosis and maintenance of embedded subsystems, the operator is able to plan maintenance operations (and therefore to optimize the availability and quality of network service), the logistics and parts supply.

Objectives: 

The objective of this project was to develop diagnostic and prognostic tools of three important parts of railway rolling stock (air conditioning, brakes and doors) that will give their real potential for use in real time before failure, thus introducing improved conditions for carrying out maintenance and traffic regularity. It will provide more detailed knowledge of the time behaviour and aging of material as well as some answers for optimizing maintenance standards.

Methodology: 

The first component of this project concerned the dynamic diagnosis by pattern recognition.

The second part was devoted to forecast preventive maintenance (or prognosis).

Institution Type:
Institution Name: 
L'Agence nationale de la recherche (The French National Research Agency)
Type of funding:
Key Results: 

Specific algorithms and a tailored model obtained provided an initial estimate of the residual life span and updates it with each new observation of the system operating state, taking into account the possible existence of several dynamics in the degradation process.

Partners: 
  • FAIVELEY TRANSPORT
  • CNRS Heudiasyc
  • IFSTTAR - Grettia
  • Keolis
  • UTT - LM2S Université de technologie de Troyes / Institut Charles Delaunay / LM2S
Organisation: 
Ifsttar - Grettia
Address: 
14-20 Boulevard Newton
Zipcode: 
77447
City: 
Marne-la-Vallée Cedex 2
Contact country: