Equipment downtime and inefficiencies are among the most significant losses manufacturers face. This project is designed to overcome these challenges by developing a smart equipment monitoring system. It will use IoT sensors and AI-driven predictive maintenance models to allow for real-time tracking of equipment performance, early detection of potential failures, and proactive planning of maintenance.
Week 1-2: Requirement Analysis and System Design
Activities: Gather requirements, define project objectives and design system architecture.
Deliverables: System architecture and project plan.
Week 3-4: Sensor Deployment and Data Collection Setup
Activities: Install IoT sensors and set up the data collection pipeline.
Deliverables: Functional IoT sensor network.
Week 5-6: AI Model Development
Activities: Develop and train predictive maintenance models using historical data.
Deliverables: Tested AI models for failure prediction.
Week 7-8: System Integration and Dashboard Design
Activities: Integrate AI models and sensors with the monitoring dashboard.
Deliverables: Interactive monitoring dashboard prototype.
Week 9-10: Testing and Validation
Activities: Test system functionality in real manufacturing scenarios and validate performance.
Deliverables: System testing reports and performance validation.
Week 11-12: Deployment and Training
Activities: Deploy the system on the manufacturing floor and train users.
Deliverables: Live system and trained staff.
