Grant: Alliance Grants
The University of Western Ontario
$90,000.00
Smart Railway: automated defect detection and predictive maintenance for resilient railway infrastructure
FY 2024-25
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.
Program
Alliance Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
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
2024
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
1003
Research subject group en
Civil engineering
Research subject group fr
Génie civil
Research subject en
Transportation engineering
Research subject fr
Génie du transport
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Smart Railway: automated defect detection and predictive maintenance for resilient railway infrastructure
Keywords
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.