Course Introduction

Predictive maintenance has become an essential strategy for infrastructure owners seeking to reduce downtime, optimize costs, and extend asset life. Unlike traditional reactive or preventive approaches, predictive maintenance leverages data, condition monitoring, and analytics to anticipate failures before they occur.

This course provides participants with a practical understanding of predictive maintenance strategies and their application to roads, utilities, and public infrastructure assets, supporting data-driven decision-making within public works authorities.

Course Objectives

Participants will be able to:

• Understand predictive maintenance concepts and benefits
• Identify suitable assets for predictive maintenance approaches
• Use condition monitoring data to anticipate failures
• Improve maintenance planning and resource allocation
• Reduce lifecycle costs and unplanned downtime

Target Audience

• Infrastructure and civil engineers
• Maintenance and operations professionals
• Asset management teams
• Project and facilities managers
• Engineers working in public works authorities
• Technical supervisors responsible for asset performance

Course Outline (5 Training Days)

Day 1: Maintenance Strategies for Infrastructure Assets

• Reactive, preventive, and predictive maintenance concepts
• Limitations of traditional maintenance approaches
• Benefits of predictive maintenance
• Asset criticality analysis
• Maintenance strategy selection

Day 2: Condition Monitoring and Data Collection

• Inspection and monitoring techniques
• Sensors and monitoring technologies
• Data collection methods for infrastructure assets
• Data quality and reliability
• Practical examples of condition monitoring

Day 3: Predictive Maintenance Planning

• Failure prediction concepts
• Maintenance forecasting techniques
• Integrating predictive data into maintenance plans
• Resource and cost optimization
• Case studies in infrastructure maintenance

Day 4: Implementation and Performance Measurement

• Implementing predictive maintenance programs
• Performance indicators and KPIs
• Managing change in maintenance practices
• Integration with asset management systems
• Continuous improvement approaches

Day 5: Optimization and Best Practices

• Maintenance optimization strategies
• Lessons learned from predictive maintenance projects
• Risk management in maintenance planning
• Practical workshops and exercises
• Program review and applied discussions