Public infrastructures, like bridges and roads, should be regularly maintained to ensure safety and function. Traditionally, fixed-interval schedules or reactive maintenance systems are followed, making them highly inefficient and leading to expenditure. In this project, a predictive maintenance system will be developed to ascertain the need for maintenance using real-time data from IoT sensors and historical maintenance records. This means that predictive maintenance using IoT can help governments schedule maintenance time efficiently, save resources, and maximize safety and life expectancy for public assets.
Week 1-2: Initial Planning and Requirement Analysis
Define project objectives, scope, and high-level requirements.
Gather all necessary data and resources Week 3-4: Design Phase
System architecture and data flow design
Dashboard interface design by wireframing and mock-ups Week 5-6: Development Phase - IoT Sensors and Data Processing
IoT sensor installation for infrastructure condition monitoring
Develop the data processing system on how sensor data was collected and stored Week 7-8: Development Phase — Machine Learning Models
Implement machine learning models that forecast maintenance needs
Train and validate models against historical data for maintenance Week 9-10: Dashboard Development and Integration
Build a monitoring dashboard that can enable real-time decision-making
Integrate into existing maintenance management systems Week 11-12: Testing and Refinement Phase
Integration testing to ensure functionality and usability
System refinement by performance metrics and user feedback
Compilation of final project report and documentation; presentation of individual reports by students.
