Grant: Alliance Grants
The University of Western Ontario
$90,000.00
Smart Railway: automated defect detection and predictive maintenance for resilient railway infrastructure
EF 2023-24
Résumé
The project aims to build an intelligent railway infrastructure monitoring platform, and preventive maintenance to improve infrastructure resilience and safety. The objectives of this project are 1) To establish light-weight computer vision algorithms to interpret image and video data of visible defects for rail track components and foundation; 2) To develop multi-dimensional anomaly detection and pattern recognition algorithms for invisible defects and early warning system of rail track and foundation conditions; 3) To develop short-term and long-term predictive maintenance models for dynamic maintenance schedules and risk mitigation plan. The project will be carried out jointly by Western University, ApoSys Technologies Inc. and National Research Council of Canada. The collaborative project will yield new concepts, methodology (techniques and algorithms) and planning implications to timely identify potential faults and defects in railway infrastructure before they lead to a failure, allowing maintenance to be scheduled at convenient times, minimizing the impact on operations and reducing downtime and cost with delays or overlooked maintenance. The data and models analyzed through the project can help inform decision-making of investments in railway infrastructure, allowing for targeted and cost-effective maintenance and upgrades. By monitoring safety and enabling more efficient use of resources, the research can contribute to a more safe, sustainable and robust railway system. Today, around 70% of all intercity surface freight and half of Canada's exports are moved by rail. In 2021, Canada's railways invested $2.3 billion in new capital programs for growth and service enhancements, where track and roadway reflected 40% of capital expenditures. By developing technologies and practices, the project will advance the Canadian railway system to remain safe and effective, support economic growth and improve the supply chain efficiency. The forefront of research also strengthens Canadian infrastructure resilience and sustainability pathways and brings together the goals of a safer, reliable and resilient railway system and infrastructure.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
586566-2023
Name nom
Tang, Yili Y
Department département
Civil and Environmental Engineering
Organization id
36
Institution établissement
The University of Western Ontario
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
90000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
700
Area of application group en
Transportation systems and services
Area of application group fr
Services et systèmes de transport
Area of application en
Transportation systems and services
Area of application fr
Services et systèmes de transport
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Not available
Research subject en
Non disponible
Research subject fr
Non disponible
Application title
Smart Railway: automated defect detection and predictive maintenance for resilient railway infrastructure
Keyword
Anomaly and pattern recognitions; Computer vision; Predictive maintenance; Rail track and foundation defects; Railway infrastructure; Resilience
Application summary
The project aims to build an intelligent railway infrastructure monitoring platform, and preventive maintenance to improve infrastructure resilience and safety. The objectives of this project are 1) To establish light-weight computer vision algorithms to interpret image and video data of visible defects for rail track components and foundation; 2) To develop multi-dimensional anomaly detection and pattern recognition algorithms for invisible defects and early warning system of rail track and foundation conditions; 3) To develop short-term and long-term predictive maintenance models for dynamic maintenance schedules and risk mitigation plan. The project will be carried out jointly by Western University, ApoSys Technologies Inc. and National Research Council of Canada. The collaborative project will yield new concepts, methodology (techniques and algorithms) and planning implications to timely identify potential faults and defects in railway infrastructure before they lead to a failure, allowing maintenance to be scheduled at convenient times, minimizing the impact on operations and reducing downtime and cost with delays or overlooked maintenance. The data and models analyzed through the project can help inform decision-making of investments in railway infrastructure, allowing for targeted and cost-effective maintenance and upgrades. By monitoring safety and enabling more efficient use of resources, the research can contribute to a more safe, sustainable and robust railway system. Today, around 70% of all intercity surface freight and half of Canada's exports are moved by rail. In 2021, Canada's railways invested $2.3 billion in new capital programs for growth and service enhancements, where track and roadway reflected 40% of capital expenditures. By developing technologies and practices, the project will advance the Canadian railway system to remain safe and effective, support economic growth and improve the supply chain efficiency. The forefront of research also strengthens Canadian infrastructure resilience and sustainability pathways and brings together the goals of a safer, reliable and resilient railway system and infrastructure.