National Research Council CanadaSkip all menusSkip first menu Menu
National Research Council Canada Government of Canada
NRC-IIT - Institute for Information Technology
NRC-IIT - Institute for Information Technology
Research Programs
3D Technologies
Artificial Intelligence Technologies
AeroSpace Data Miner (ADAM)
BioIntelligence
Data Mining in Functional Genomics (BioMine)
Automated Case Based Reasoning (CBR)
Fuzzy Extension to the CLIPS Expert System Shell (FuzzyCLIPS)
Java library for Building Fuzzy Systems (FuzzyJ Toolkit)
Integrated Diagnostic System (IDS)
Intelligent Optimization of Industrial Operations (IO2)
Knowledge from Structured Documents (KSD)
Wheel Impact Load Detector Miner (WILDMiner)
Broadband Visual Communication
Computational Video
e-Learning
Health Initiative
High Performance Computing
Human-Computer Interaction
Information Analysis and Retrieval
Interactive Language Technologies
Internet Logic
People-Centred Technologies
Security and Privacy
Software Engineering
Research in NRC-IIT Locations
Research Success Stories
Printable version Printable
version
Home | Research | Research Programs | Artificial Intelligence Technologies | Wheel Impact Load Detector Miner (WILDMiner)

Artificial Intelligence Technologies

Wheel Impact Load Detector Miner (WILDMiner)

The railway industry has undergone a remarkable technological revolution during the past ten years. The companies have adopted a number of new technologies to optimize their productivity and minimize the costs of their operation. A notable example is Wheel Impact Load Detector (WILD) system, a system that makes it possible to detect which wheels will cause impacts that are high enough to damage the railway track. The WILD system consists of sensors installed at the strategic places along the track. When a train passes near the sensors, the latter measure the impact caused by each wheel on the track. The data that is gathered is automatically transmitted to the company's central system. When a wheel causes too high an impact, the train must reduce its speed and then stop at the nearest shop to uncouple the car whose wheel is damaged. This greatly help minimizing the damage to the tracks and bridges. However, reductions in the speed of the trains and the obligation to stop at the shops introduce many delays in the delivery of the goods and reduce the throughput of the company.

The objective of the WILDMiner project is to develop the models and systems required to integrate a proactive approach to wheel maintenance. The developed models will predict which wheels are likely to break. The maintenance crew will use this information to plan and perform wheel replacements before the wheels become so damaged that they cause service interruptions. This should reduce operational costs considerably.

The WILDMiner project is currently in its first stage, which is to perform a feasibility study. In this first phase researchers study the possibility of using Data Mining techniques, similar to those employed in the ADAM project, to develop the required models. Some preliminary models have been developed and the results appear promising.

Research Contact

Dr. Sylvain Létourneau
Research Officer
Integrated Reasoning

NRC Institute for Information Technology
1200 Montreal Road
Building M-50, Room 267A
Ottawa, ON K1A 0R6
Telephone: +1 (613) 990-1178
Fax: +1 (613) 952-0215
E-mail: Sylvain Létourneau

Business Contact

Dr. George Forester
Business Development Officer
Business Development Office, NCR

NRC Institute for Information Technology
1200 Montreal Road
Building M-50, Room 203
Ottawa, ON K1A 0R6
Telephone: +1 (613) 993-3478
Fax: +1 (613) 952-0074
E-mail: George.Forester@nrc-cnrc.gc.ca


Date Published: 2002-12-31
Top of Page